Project
Loading the data
carbonData<-read.csv('/Users/angadsingh/Downloads/Carbon Emission.csv')
summary(carbonData) Body.Type Sex Diet How.Often.Shower Heating.Energy.Source Transport Vehicle.Type Social.Activity
Length:10000 Length:10000 Length:10000 Length:10000 Length:10000 Length:10000 Length:10000 Length:10000
Class :character Class :character Class :character Class :character Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character Mode :character Mode :character Mode :character Mode :character
Monthly.Grocery.Bill Frequency.of.Traveling.by.Air Vehicle.Monthly.Distance.Km Waste.Bag.Size Waste.Bag.Weekly.Count How.Long.TV.PC.Daily.Hour
Min. : 50.0 Length:10000 Min. : 0 Length:10000 Min. :1.000 Min. : 0.00
1st Qu.:111.0 Class :character 1st Qu.: 69 Class :character 1st Qu.:2.000 1st Qu.: 6.00
Median :173.0 Mode :character Median : 823 Mode :character Median :4.000 Median :12.00
Mean :173.9 Mean :2031 Mean :4.025 Mean :12.14
3rd Qu.:237.0 3rd Qu.:2517 3rd Qu.:6.000 3rd Qu.:18.00
Max. :299.0 Max. :9999 Max. :7.000 Max. :24.00
How.Many.New.Clothes.Monthly How.Long.Internet.Daily.Hour Energy.efficiency Recycling Cooking_With CarbonEmission
Min. : 0.00 Min. : 0.00 Length:10000 Length:10000 Length:10000 Min. : 306
1st Qu.:13.00 1st Qu.: 6.00 Class :character Class :character Class :character 1st Qu.:1538
Median :25.00 Median :12.00 Mode :character Mode :character Mode :character Median :2080
Mean :25.11 Mean :11.89 Mean :2269
3rd Qu.:38.00 3rd Qu.:18.00 3rd Qu.:2768
Max. :50.00 Max. :24.00 Max. :8377 str(carbonData)'data.frame': 10000 obs. of 20 variables:
$ Body.Type : chr "overweight" "obese" "overweight" "overweight" ...
$ Sex : chr "female" "female" "male" "male" ...
$ Diet : chr "pescatarian" "vegetarian" "omnivore" "omnivore" ...
$ How.Often.Shower : chr "daily" "less frequently" "more frequently" "twice a day" ...
$ Heating.Energy.Source : chr "coal" "natural gas" "wood" "wood" ...
$ Transport : chr "public" "walk/bicycle" "private" "walk/bicycle" ...
$ Vehicle.Type : chr "" "" "petrol" "" ...
$ Social.Activity : chr "often" "often" "never" "sometimes" ...
$ Monthly.Grocery.Bill : int 230 114 138 157 266 144 56 59 200 135 ...
$ Frequency.of.Traveling.by.Air: chr "frequently" "rarely" "never" "rarely" ...
$ Vehicle.Monthly.Distance.Km : int 210 9 2472 74 8457 658 5363 54 1376 440 ...
$ Waste.Bag.Size : chr "large" "extra large" "small" "medium" ...
$ Waste.Bag.Weekly.Count : int 4 3 1 3 1 1 4 3 3 1 ...
$ How.Long.TV.PC.Daily.Hour : int 7 9 14 20 3 22 9 5 3 8 ...
$ How.Many.New.Clothes.Monthly : int 26 38 47 5 5 18 11 39 31 23 ...
$ How.Long.Internet.Daily.Hour : int 1 5 6 7 6 9 19 15 15 18 ...
$ Energy.efficiency : chr "No" "No" "Sometimes" "Sometimes" ...
$ Recycling : chr "['Metal']" "['Metal']" "['Metal']" "['Paper', 'Plastic', 'Glass', 'Metal']" ...
$ Cooking_With : chr "['Stove', 'Oven']" "['Stove', 'Microwave']" "['Oven', 'Microwave']" "['Microwave', 'Grill', 'Airfryer']" ...
$ CarbonEmission : int 2238 1892 2595 1074 4743 1647 1832 2322 2494 1178 ...From the str of carbon data i can see that i am having empty vehicle types as “” so i will replace them with No vehicle
carbonData$Vehicle.Type[carbonData$Transport=='public'|carbonData$Transport=='walk/bicycle']<-'FuelEfficient'
#carbonData<- carbonData %>% mutate(Vehicle.Type=ifelse(Vehicle.Type=="","No vehicle",Vehicle.Type))
str(carbonData)'data.frame': 10000 obs. of 20 variables:
$ Body.Type : chr "overweight" "obese" "overweight" "overweight" ...
$ Sex : chr "female" "female" "male" "male" ...
$ Diet : chr "pescatarian" "vegetarian" "omnivore" "omnivore" ...
$ How.Often.Shower : chr "daily" "less frequently" "more frequently" "twice a day" ...
$ Heating.Energy.Source : chr "coal" "natural gas" "wood" "wood" ...
$ Transport : chr "public" "walk/bicycle" "private" "walk/bicycle" ...
$ Vehicle.Type : chr "FuelEfficient" "FuelEfficient" "petrol" "FuelEfficient" ...
$ Social.Activity : chr "often" "often" "never" "sometimes" ...
$ Monthly.Grocery.Bill : int 230 114 138 157 266 144 56 59 200 135 ...
$ Frequency.of.Traveling.by.Air: chr "frequently" "rarely" "never" "rarely" ...
$ Vehicle.Monthly.Distance.Km : int 210 9 2472 74 8457 658 5363 54 1376 440 ...
$ Waste.Bag.Size : chr "large" "extra large" "small" "medium" ...
$ Waste.Bag.Weekly.Count : int 4 3 1 3 1 1 4 3 3 1 ...
$ How.Long.TV.PC.Daily.Hour : int 7 9 14 20 3 22 9 5 3 8 ...
$ How.Many.New.Clothes.Monthly : int 26 38 47 5 5 18 11 39 31 23 ...
$ How.Long.Internet.Daily.Hour : int 1 5 6 7 6 9 19 15 15 18 ...
$ Energy.efficiency : chr "No" "No" "Sometimes" "Sometimes" ...
$ Recycling : chr "['Metal']" "['Metal']" "['Metal']" "['Paper', 'Plastic', 'Glass', 'Metal']" ...
$ Cooking_With : chr "['Stove', 'Oven']" "['Stove', 'Microwave']" "['Oven', 'Microwave']" "['Microwave', 'Grill', 'Airfryer']" ...
$ CarbonEmission : int 2238 1892 2595 1074 4743 1647 1832 2322 2494 1178 ...#carbonData[carbonData == ""]<-NA
colSums(is.na(carbonData)) Body.Type Sex Diet How.Often.Shower Heating.Energy.Source
0 0 0 0 0
Transport Vehicle.Type Social.Activity Monthly.Grocery.Bill Frequency.of.Traveling.by.Air
0 0 0 0 0
Vehicle.Monthly.Distance.Km Waste.Bag.Size Waste.Bag.Weekly.Count How.Long.TV.PC.Daily.Hour How.Many.New.Clothes.Monthly
0 0 0 0 0
How.Long.Internet.Daily.Hour Energy.efficiency Recycling Cooking_With CarbonEmission
0 0 0 0 0 library(tidyverse)── Attaching core tidyverse packages ────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.0 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2 ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errorsparseList<-function(x){
str_remove_all(x,"\\[|\\]|'")%>%
strsplit(", ")%>%
unlist()
}
carbonData$Recycling<-sapply(carbonData$Recycling,parseList)
carbonData$Cooking_With<-sapply(carbonData$Cooking_With,parseList)
carbonData$Recycling<-sapply(carbonData$Recycling,paste,collapse=",")
carbonData$Cooking_With<-sapply(carbonData$Cooking_With,paste,collapse=",")
#str(carbonData)
dummies<-function(col){
items<-unlist(str_split(col,","))
items<-trimws(items)
items<-items[items != ""]
uniqueItems<-unique(items)
dummyDataFrame<-data.frame(matrix(0,nrow = length(col),ncol = length(uniqueItems)))
colnames(dummyDataFrame)<-uniqueItems
for (i in seq_along(col)) {
rowItems<-unlist(str_split(col[i],","))%>%
map_chr(~str_trim(.))%>%
discard(~.=="")
rowItems<-rowItems[rowItems %in% uniqueItems]
dummyDataFrame[i,rowItems]<-1
}
return(dummyDataFrame)
}
recyclingDummies<-dummies(carbonData$Recycling)
cookingDummies<-dummies(carbonData$Cooking_With)
carbonData<-cbind(carbonData,recyclingDummies,cookingDummies)
carbonData$Recycling<- NULL
carbonData$Cooking_With<-NULL
str(carbonData)'data.frame': 10000 obs. of 27 variables:
$ Body.Type : chr "overweight" "obese" "overweight" "overweight" ...
$ Sex : chr "female" "female" "male" "male" ...
$ Diet : chr "pescatarian" "vegetarian" "omnivore" "omnivore" ...
$ How.Often.Shower : chr "daily" "less frequently" "more frequently" "twice a day" ...
$ Heating.Energy.Source : chr "coal" "natural gas" "wood" "wood" ...
$ Transport : chr "public" "walk/bicycle" "private" "walk/bicycle" ...
$ Vehicle.Type : chr "FuelEfficient" "FuelEfficient" "petrol" "FuelEfficient" ...
$ Social.Activity : chr "often" "often" "never" "sometimes" ...
$ Monthly.Grocery.Bill : int 230 114 138 157 266 144 56 59 200 135 ...
$ Frequency.of.Traveling.by.Air: chr "frequently" "rarely" "never" "rarely" ...
$ Vehicle.Monthly.Distance.Km : int 210 9 2472 74 8457 658 5363 54 1376 440 ...
$ Waste.Bag.Size : chr "large" "extra large" "small" "medium" ...
$ Waste.Bag.Weekly.Count : int 4 3 1 3 1 1 4 3 3 1 ...
$ How.Long.TV.PC.Daily.Hour : int 7 9 14 20 3 22 9 5 3 8 ...
$ How.Many.New.Clothes.Monthly : int 26 38 47 5 5 18 11 39 31 23 ...
$ How.Long.Internet.Daily.Hour : int 1 5 6 7 6 9 19 15 15 18 ...
$ Energy.efficiency : chr "No" "No" "Sometimes" "Sometimes" ...
$ CarbonEmission : int 2238 1892 2595 1074 4743 1647 1832 2322 2494 1178 ...
$ Metal : num 1 1 1 1 0 1 0 0 0 0 ...
$ Paper : num 0 0 0 1 1 1 0 1 0 0 ...
$ Plastic : num 0 0 0 1 0 0 0 1 0 0 ...
$ Glass : num 0 0 0 1 0 1 0 1 1 1 ...
$ Stove : num 1 1 0 0 0 1 0 1 0 0 ...
$ Oven : num 1 0 1 0 1 1 0 0 0 0 ...
$ Microwave : num 0 1 1 1 0 1 0 1 1 1 ...
$ Grill : num 0 0 0 1 0 0 1 0 1 1 ...
$ Airfryer : num 0 0 0 1 0 0 1 0 1 1 ...carbonData<-carbonData %>%
mutate_if(is.character, as.factor)%>%
mutate_if(is.integer, as.numeric)
str(carbonData)'data.frame': 10000 obs. of 27 variables:
$ Body.Type : Factor w/ 4 levels "normal","obese",..: 3 2 3 3 2 3 4 4 3 4 ...
$ Sex : Factor w/ 2 levels "female","male": 1 1 2 2 1 2 1 1 2 1 ...
$ Diet : Factor w/ 4 levels "omnivore","pescatarian",..: 2 4 1 1 4 4 3 3 1 2 ...
$ How.Often.Shower : Factor w/ 4 levels "daily","less frequently",..: 1 2 3 4 1 2 2 3 1 1 ...
$ Heating.Energy.Source : Factor w/ 4 levels "coal","electricity",..: 1 3 4 4 1 4 4 1 4 4 ...
$ Transport : Factor w/ 3 levels "private","public",..: 2 3 1 3 1 2 1 3 2 2 ...
$ Vehicle.Type : Factor w/ 6 levels "diesel","electric",..: 3 3 6 3 1 3 4 3 3 3 ...
$ Social.Activity : Factor w/ 3 levels "never","often",..: 2 2 1 3 2 3 1 3 1 2 ...
$ Monthly.Grocery.Bill : num 230 114 138 157 266 144 56 59 200 135 ...
$ Frequency.of.Traveling.by.Air: Factor w/ 4 levels "frequently","never",..: 1 3 2 3 4 1 3 4 1 3 ...
$ Vehicle.Monthly.Distance.Km : num 210 9 2472 74 8457 ...
$ Waste.Bag.Size : Factor w/ 4 levels "extra large",..: 2 1 4 3 2 2 3 1 3 1 ...
$ Waste.Bag.Weekly.Count : num 4 3 1 3 1 1 4 3 3 1 ...
$ How.Long.TV.PC.Daily.Hour : num 7 9 14 20 3 22 9 5 3 8 ...
$ How.Many.New.Clothes.Monthly : num 26 38 47 5 5 18 11 39 31 23 ...
$ How.Long.Internet.Daily.Hour : num 1 5 6 7 6 9 19 15 15 18 ...
$ Energy.efficiency : Factor w/ 3 levels "No","Sometimes",..: 1 1 2 2 3 2 2 1 3 2 ...
$ CarbonEmission : num 2238 1892 2595 1074 4743 ...
$ Metal : num 1 1 1 1 0 1 0 0 0 0 ...
$ Paper : num 0 0 0 1 1 1 0 1 0 0 ...
$ Plastic : num 0 0 0 1 0 0 0 1 0 0 ...
$ Glass : num 0 0 0 1 0 1 0 1 1 1 ...
$ Stove : num 1 1 0 0 0 1 0 1 0 0 ...
$ Oven : num 1 0 1 0 1 1 0 0 0 0 ...
$ Microwave : num 0 1 1 1 0 1 0 1 1 1 ...
$ Grill : num 0 0 0 1 0 0 1 0 1 1 ...
$ Airfryer : num 0 0 0 1 0 0 1 0 1 1 ...summary(carbonData) Body.Type Sex Diet How.Often.Shower Heating.Energy.Source Transport Vehicle.Type Social.Activity
normal :2473 female:5007 omnivore :2492 daily :2546 coal :2523 private :3279 diesel : 622 never :3406
obese :2500 male :4993 pescatarian:2554 less frequently:2487 electricity:2552 public :3294 electric : 671 often :3319
overweight :2487 vegan :2497 more frequently:2451 natural gas:2462 walk/bicycle:3427 FuelEfficient:6721 sometimes:3275
underweight:2540 vegetarian :2457 twice a day :2516 wood :2463 hybrid : 642
lpg : 697
petrol : 647
Monthly.Grocery.Bill Frequency.of.Traveling.by.Air Vehicle.Monthly.Distance.Km Waste.Bag.Size Waste.Bag.Weekly.Count How.Long.TV.PC.Daily.Hour
Min. : 50.0 frequently :2524 Min. : 0 extra large:2500 Min. :1.000 Min. : 0.00
1st Qu.:111.0 never :2459 1st Qu.: 69 large :2501 1st Qu.:2.000 1st Qu.: 6.00
Median :173.0 rarely :2477 Median : 823 medium :2474 Median :4.000 Median :12.00
Mean :173.9 very frequently:2540 Mean :2031 small :2525 Mean :4.025 Mean :12.14
3rd Qu.:237.0 3rd Qu.:2517 3rd Qu.:6.000 3rd Qu.:18.00
Max. :299.0 Max. :9999 Max. :7.000 Max. :24.00
How.Many.New.Clothes.Monthly How.Long.Internet.Daily.Hour Energy.efficiency CarbonEmission Metal Paper Plastic
Min. : 0.00 Min. : 0.00 No :3221 Min. : 306 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:13.00 1st Qu.: 6.00 Sometimes:3463 1st Qu.:1538 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
Median :25.00 Median :12.00 Yes :3316 Median :2080 Median :1.0000 Median :0.0000 Median :0.0000
Mean :25.11 Mean :11.89 Mean :2269 Mean :0.5047 Mean :0.4977 Mean :0.4997
3rd Qu.:38.00 3rd Qu.:18.00 3rd Qu.:2768 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :50.00 Max. :24.00 Max. :8377 Max. :1.0000 Max. :1.0000 Max. :1.0000
Glass Stove Oven Microwave Grill Airfryer
Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
Median :0.0000 Median :1.0000 Median :1.000 Median :1.0000 Median :0.0000 Median :0.0000
Mean :0.4979 Mean :0.5041 Mean :0.505 Mean :0.5073 Mean :0.4992 Mean :0.4992
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.0000 Max. :1.0000 table(carbonData$Body.Type)
normal obese overweight underweight
2473 2500 2487 2540 table(carbonData$Sex)
female male
5007 4993 table(carbonData$Diet)
omnivore pescatarian vegan vegetarian
2492 2554 2497 2457 table(carbonData$How.Often.Shower)
daily less frequently more frequently twice a day
2546 2487 2451 2516 table(carbonData$Heating.Energy.Source)
coal electricity natural gas wood
2523 2552 2462 2463 table(carbonData$Transport)
private public walk/bicycle
3279 3294 3427 table(carbonData$Social.Activity)
never often sometimes
3406 3319 3275 table(carbonData$Frequency.of.Traveling.by.Air)
frequently never rarely very frequently
2524 2459 2477 2540 table(carbonData$Waste.Bag.Size)
extra large large medium small
2500 2501 2474 2525 table(carbonData$Energy.efficiency)
No Sometimes Yes
3221 3463 3316 hist(carbonData$CarbonEmission)
carbonData$CarbonEmission<-log(carbonData$CarbonEmission)
hist(carbonData$CarbonEmission)
carbonIndices<-which(names(carbonData)=='CarbonEmission')
for (c in colnames(carbonData[,-carbonIndices])) {
if(is.factor(carbonData[,c])){
try({
anovaaResult<-aov(carbonData$CarbonEmission~carbonData[,c])
cat("ANOVA of ",c, "and CarbonEmission", "\n")
print(summary(anovaaResult))
boxplot(carbonData$CarbonEmission~carbonData[,c],shade=TRUE, main = paste("Carbon Emission vs", c), xlab ="CarbonEmission", ylab=c ,col="lightgreen")
})
}
else if (is.numeric(carbonData[,c])){
try({
corTest<-cor.test(carbonData$CarbonEmission,carbonData[,c], method = "pearson")
cat("p.value of ",c, "and Carbon Emission", corTest$p.value, "\n")
plot(carbonData$CarbonEmission,carbonData[,c], main = paste("Carbon Emission vs", c), xlab ="Carbon Emission", ylab=c)
})
}
}ANOVA of Body.Type and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 3 82.7 27.583 149 <2e-16 ***
Residuals 9996 1850.3 0.185
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ANOVA of Sex and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 1 55.5 55.51 295.6 <2e-16 ***
Residuals 9998 1877.5 0.19
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ANOVA of Diet and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 3 11.8 3.944 20.52 2.98e-13 ***
Residuals 9996 1921.2 0.192
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ANOVA of How.Often.Shower and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 3 1.9 0.6333 3.278 0.0201 *
Residuals 9996 1931.1 0.1932
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ANOVA of Heating.Energy.Source and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 3 71.1 23.702 127.2 <2e-16 ***
Residuals 9996 1861.9 0.186
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ANOVA of Transport and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 2 384.7 192.34 1242 <2e-16 ***
Residuals 9997 1548.3 0.15
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ANOVA of Vehicle.Type and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 5 573.6 114.71 843.3 <2e-16 ***
Residuals 9994 1359.5 0.14
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ANOVA of Social.Activity and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 2 8.5 4.248 22.07 2.74e-10 ***
Residuals 9997 1924.5 0.193
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
p.value of Monthly.Grocery.Bill and Carbon Emission 8.380793e-21 
ANOVA of Frequency.of.Traveling.by.Air and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 3 568.2 189.41 1387 <2e-16 ***
Residuals 9996 1364.8 0.14
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
p.value of Vehicle.Monthly.Distance.Km and Carbon Emission 0 
ANOVA of Waste.Bag.Size and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 3 53.1 17.692 94.07 <2e-16 ***
Residuals 9996 1879.9 0.188
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
p.value of Waste.Bag.Weekly.Count and Carbon Emission 3.257926e-79 
p.value of How.Long.TV.PC.Daily.Hour and Carbon Emission 0.2282313 
p.value of How.Many.New.Clothes.Monthly and Carbon Emission 8.851718e-131 
p.value of How.Long.Internet.Daily.Hour and Carbon Emission 1.411988e-09 
ANOVA of Energy.efficiency and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 2 0.7 0.3284 1.699 0.183
Residuals 9997 1932.4 0.1933 
p.value of Metal and Carbon Emission 9.628913e-17 
p.value of Paper and Carbon Emission 2.622474e-20 
p.value of Plastic and Carbon Emission 3.357259e-07 
p.value of Glass and Carbon Emission 3.371649e-08 
p.value of Stove and Carbon Emission 0.3583156 
p.value of Oven and Carbon Emission 0.001109564 
p.value of Microwave and Carbon Emission 0.7301457 
p.value of Grill and Carbon Emission 0.06209031 
p.value of Airfryer and Carbon Emission 0.06209031 
knnModel<-train(CarbonEmission~.,data = carbonTrainData, method="knn", trControl=trainControl(method = "cv", number=5))knnModelk-Nearest Neighbors
8001 samples
26 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6401, 6401, 6400, 6401, 6401
Resampling results across tuning parameters:
k RMSE Rsquared MAE
5 0.3912082 0.2416923 0.3099291
7 0.3820210 0.2612955 0.3027079
9 0.3789765 0.2674213 0.3010474
RMSE was used to select the optimal model using the smallest value.
The final value used for the model was k = 9.knnPred<-predict(knnModel,newdata = carbonTestData)
rmse=function(x,y){
return((mean(x-y)^2)^0.5)
}
rmse(knnPred,carbonTestLabels)[1] 0.0004033061lmModel<-train(CarbonEmission~.,data = carbonTrainData, method="lm", trControl=trainControl(method = "cv", number=5))
lmModelLinear Regression
8001 samples
26 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6401, 6401, 6401, 6400, 6401
Resampling results:
RMSE Rsquared MAE
0.1216956 0.9235284 0.08674322
Tuning parameter 'intercept' was held constant at a value of TRUEsummary(lmModel)
Call:
lm(formula = .outcome ~ ., data = dat)
Residuals:
Min 1Q Median 3Q Max
-0.77905 -0.05477 0.00826 0.06510 0.47201
Coefficients: (2 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.409e+00 1.167e-02 634.830 < 2e-16 ***
Body.Typeobese 1.843e-01 3.854e-03 47.825 < 2e-16 ***
Body.Typeoverweight 9.380e-02 3.854e-03 24.337 < 2e-16 ***
Body.Typeunderweight -4.979e-02 3.841e-03 -12.964 < 2e-16 ***
Sexmale 1.511e-01 2.721e-03 55.551 < 2e-16 ***
Dietpescatarian -4.430e-02 3.843e-03 -11.529 < 2e-16 ***
Dietvegan -7.990e-02 3.860e-03 -20.701 < 2e-16 ***
Dietvegetarian -7.180e-02 3.871e-03 -18.548 < 2e-16 ***
`How.Often.Showerless frequently` -7.940e-03 3.827e-03 -2.075 0.03803 *
`How.Often.Showermore frequently` 1.786e-02 3.850e-03 4.638 3.57e-06 ***
`How.Often.Showertwice a day` 1.058e-02 3.838e-03 2.755 0.00588 **
Heating.Energy.Sourceelectricity -2.236e-01 3.809e-03 -58.694 < 2e-16 ***
`Heating.Energy.Sourcenatural gas` -9.702e-02 3.843e-03 -25.244 < 2e-16 ***
Heating.Energy.Sourcewood -9.863e-02 3.866e-03 -25.513 < 2e-16 ***
Transportpublic -1.970e-01 6.834e-03 -28.834 < 2e-16 ***
`Transportwalk/bicycle` -1.722e-01 7.231e-03 -23.809 < 2e-16 ***
Vehicle.Typeelectric -5.033e-01 7.590e-03 -66.310 < 2e-16 ***
Vehicle.TypeFuelEfficient NA NA NA NA
Vehicle.Typehybrid -1.377e-01 7.638e-03 -18.032 < 2e-16 ***
Vehicle.Typelpg 3.441e-02 7.609e-03 4.523 6.19e-06 ***
Vehicle.Typepetrol 1.909e-01 7.639e-03 24.994 < 2e-16 ***
Social.Activityoften 8.625e-02 3.329e-03 25.912 < 2e-16 ***
Social.Activitysometimes 3.894e-02 3.326e-03 11.707 < 2e-16 ***
Monthly.Grocery.Bill 4.704e-04 1.873e-05 25.120 < 2e-16 ***
Frequency.of.Traveling.by.Airnever -3.610e-01 3.865e-03 -93.387 < 2e-16 ***
Frequency.of.Traveling.by.Airrarely -2.413e-01 3.838e-03 -62.885 < 2e-16 ***
`Frequency.of.Traveling.by.Airvery frequently` 2.643e-01 3.815e-03 69.276 < 2e-16 ***
Vehicle.Monthly.Distance.Km 6.730e-05 8.023e-07 83.884 < 2e-16 ***
Waste.Bag.Sizelarge -6.017e-02 3.833e-03 -15.698 < 2e-16 ***
Waste.Bag.Sizemedium -1.264e-01 3.841e-03 -32.917 < 2e-16 ***
Waste.Bag.Sizesmall -1.948e-01 3.849e-03 -50.609 < 2e-16 ***
Waste.Bag.Weekly.Count 4.161e-02 6.827e-04 60.946 < 2e-16 ***
How.Long.TV.PC.Daily.Hour 1.272e-03 1.909e-04 6.662 2.89e-11 ***
How.Many.New.Clothes.Monthly 7.067e-03 9.244e-05 76.452 < 2e-16 ***
How.Long.Internet.Daily.Hour 3.958e-03 1.869e-04 21.177 < 2e-16 ***
Energy.efficiencySometimes -2.072e-02 3.322e-03 -6.236 4.73e-10 ***
Energy.efficiencyYes -3.183e-02 3.370e-03 -9.444 < 2e-16 ***
Metal -6.951e-02 2.722e-03 -25.540 < 2e-16 ***
Paper -7.439e-02 2.720e-03 -27.347 < 2e-16 ***
Plastic -2.879e-02 2.722e-03 -10.575 < 2e-16 ***
Glass -4.867e-02 2.718e-03 -17.907 < 2e-16 ***
Stove 1.493e-02 2.719e-03 5.492 4.10e-08 ***
Oven 1.834e-02 2.721e-03 6.743 1.67e-11 ***
Microwave 7.785e-03 2.719e-03 2.863 0.00420 **
Grill 1.791e-02 2.719e-03 6.589 4.72e-11 ***
Airfryer NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1213 on 7957 degrees of freedom
Multiple R-squared: 0.9243, Adjusted R-squared: 0.9239
F-statistic: 2260 on 43 and 7957 DF, p-value: < 2.2e-16stepwiseModel<-train(CarbonEmission~.,data = carbonTrainData, method="leapBackward", trControl=trainControl(method = "cv", number=5))Warning: 2 linear dependencies foundReordering variables and trying again:Warning: 2 linear dependencies foundReordering variables and trying again:Warning: 2 linear dependencies foundReordering variables and trying again:Warning: 2 linear dependencies foundReordering variables and trying again:Warning: 2 linear dependencies foundReordering variables and trying again:Warning: 2 linear dependencies foundReordering variables and trying again:stepwiseModelLinear Regression with Backwards Selection
8001 samples
26 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6401, 6401, 6400, 6401, 6401
Resampling results across tuning parameters:
nvmax RMSE Rsquared MAE
2 0.3760164 0.2691345 0.2992884
3 0.3594632 0.3318216 0.2867232
4 0.3460790 0.3807424 0.2727536
RMSE was used to select the optimal model using the smallest value.
The final value used for the model was nvmax = 4.summary(stepwiseModel$finalModel)Subset selection object
45 Variables (and intercept)
Forced in Forced out
Body.Typeobese FALSE FALSE
Body.Typeoverweight FALSE FALSE
Body.Typeunderweight FALSE FALSE
Sexmale FALSE FALSE
Dietpescatarian FALSE FALSE
Dietvegan FALSE FALSE
Dietvegetarian FALSE FALSE
How.Often.Showerless frequently FALSE FALSE
How.Often.Showermore frequently FALSE FALSE
How.Often.Showertwice a day FALSE FALSE
Heating.Energy.Sourceelectricity FALSE FALSE
Heating.Energy.Sourcenatural gas FALSE FALSE
Heating.Energy.Sourcewood FALSE FALSE
Transportpublic FALSE FALSE
Transportwalk/bicycle FALSE FALSE
Vehicle.Typeelectric FALSE FALSE
Vehicle.Typehybrid FALSE FALSE
Vehicle.Typelpg FALSE FALSE
Vehicle.Typepetrol FALSE FALSE
Social.Activityoften FALSE FALSE
Social.Activitysometimes FALSE FALSE
Monthly.Grocery.Bill FALSE FALSE
Frequency.of.Traveling.by.Airnever FALSE FALSE
Frequency.of.Traveling.by.Airrarely FALSE FALSE
Frequency.of.Traveling.by.Airvery frequently FALSE FALSE
Vehicle.Monthly.Distance.Km FALSE FALSE
Waste.Bag.Sizelarge FALSE FALSE
Waste.Bag.Sizemedium FALSE FALSE
Waste.Bag.Sizesmall FALSE FALSE
Waste.Bag.Weekly.Count FALSE FALSE
How.Long.TV.PC.Daily.Hour FALSE FALSE
How.Many.New.Clothes.Monthly FALSE FALSE
How.Long.Internet.Daily.Hour FALSE FALSE
Energy.efficiencySometimes FALSE FALSE
Energy.efficiencyYes FALSE FALSE
Metal FALSE FALSE
Paper FALSE FALSE
Plastic FALSE FALSE
Glass FALSE FALSE
Stove FALSE FALSE
Oven FALSE FALSE
Microwave FALSE FALSE
Grill FALSE FALSE
Vehicle.TypeFuelEfficient FALSE FALSE
Airfryer FALSE FALSE
1 subsets of each size up to 5
Selection Algorithm: backward
Body.Typeobese Body.Typeoverweight Body.Typeunderweight Sexmale Dietpescatarian Dietvegan Dietvegetarian How.Often.Showerless frequently
1 ( 1 ) " " " " " " " " " " " " " " " "
2 ( 1 ) " " " " " " " " " " " " " " " "
3 ( 1 ) " " " " " " " " " " " " " " " "
4 ( 1 ) " " " " " " " " " " " " " " " "
5 ( 1 ) " " " " " " " " " " " " " " " "
How.Often.Showermore frequently How.Often.Showertwice a day Heating.Energy.Sourceelectricity Heating.Energy.Sourcenatural gas
1 ( 1 ) " " " " " " " "
2 ( 1 ) " " " " " " " "
3 ( 1 ) " " " " " " " "
4 ( 1 ) " " " " " " " "
5 ( 1 ) " " " " " " " "
Heating.Energy.Sourcewood Transportpublic Transportwalk/bicycle Vehicle.Typeelectric Vehicle.TypeFuelEfficient Vehicle.Typehybrid Vehicle.Typelpg
1 ( 1 ) " " " " " " " " " " " " " "
2 ( 1 ) " " " " " " " " " " " " " "
3 ( 1 ) " " " " " " "*" " " " " " "
4 ( 1 ) " " " " " " "*" " " " " " "
5 ( 1 ) " " " " " " "*" " " " " " "
Vehicle.Typepetrol Social.Activityoften Social.Activitysometimes Monthly.Grocery.Bill Frequency.of.Traveling.by.Airnever
1 ( 1 ) " " " " " " " " " "
2 ( 1 ) " " " " " " " " " "
3 ( 1 ) " " " " " " " " " "
4 ( 1 ) " " " " " " " " " "
5 ( 1 ) " " " " " " " " "*"
Frequency.of.Traveling.by.Airrarely Frequency.of.Traveling.by.Airvery frequently Vehicle.Monthly.Distance.Km Waste.Bag.Sizelarge
1 ( 1 ) " " " " "*" " "
2 ( 1 ) " " "*" "*" " "
3 ( 1 ) " " "*" "*" " "
4 ( 1 ) " " "*" "*" " "
5 ( 1 ) " " "*" "*" " "
Waste.Bag.Sizemedium Waste.Bag.Sizesmall Waste.Bag.Weekly.Count How.Long.TV.PC.Daily.Hour How.Many.New.Clothes.Monthly How.Long.Internet.Daily.Hour
1 ( 1 ) " " " " " " " " " " " "
2 ( 1 ) " " " " " " " " " " " "
3 ( 1 ) " " " " " " " " " " " "
4 ( 1 ) " " " " " " " " "*" " "
5 ( 1 ) " " " " " " " " "*" " "
Energy.efficiencySometimes Energy.efficiencyYes Metal Paper Plastic Glass Stove Oven Microwave Grill Airfryer
1 ( 1 ) " " " " " " " " " " " " " " " " " " " " " "
2 ( 1 ) " " " " " " " " " " " " " " " " " " " " " "
3 ( 1 ) " " " " " " " " " " " " " " " " " " " " " "
4 ( 1 ) " " " " " " " " " " " " " " " " " " " " " "
5 ( 1 ) " " " " " " " " " " " " " " " " " " " " " " colSums(is.na(carbonTrainData)) Body.Type Sex Diet How.Often.Shower Heating.Energy.Source
0 0 0 0 0
Transport Vehicle.Type Social.Activity Monthly.Grocery.Bill Frequency.of.Traveling.by.Air
0 0 0 0 0
Vehicle.Monthly.Distance.Km Waste.Bag.Size Waste.Bag.Weekly.Count How.Long.TV.PC.Daily.Hour How.Many.New.Clothes.Monthly
0 0 0 0 0
How.Long.Internet.Daily.Hour Energy.efficiency CarbonEmission Metal Paper
0 0 0 0 0
Plastic Glass Stove Oven Microwave
0 0 0 0 0
Grill Airfryer
0 0 #Lasso Model
library(glmnet)Loading required package: Matrix
Attaching package: ‘Matrix’
The following objects are masked from ‘package:tidyr’:
expand, pack, unpack
Loaded glmnet 4.1-8set.seed(1)
lassoModel<-train(CarbonEmission~.,data = carbonTrainData,method="glmnet",trControl= trainControl(method = "cv", number=5), tuneGrid = expand.grid(alpha=1, lambda=10^seq(-3,3,length=100))) Warning: There were missing values in resampled performance measures.lassoModelglmnet
8001 samples
26 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6401, 6401, 6401, 6400, 6401
Resampling results across tuning parameters:
lambda RMSE Rsquared MAE
1.000000e-03 0.1219858 0.9232209 0.08693488
1.149757e-03 0.1220753 0.9231430 0.08700875
1.321941e-03 0.1221904 0.9230430 0.08710667
1.519911e-03 0.1223470 0.9229054 0.08723849
1.747528e-03 0.1225536 0.9227225 0.08741574
2.009233e-03 0.1228246 0.9224806 0.08765246
2.310130e-03 0.1231771 0.9221635 0.08796258
2.656088e-03 0.1236457 0.9217360 0.08837683
3.053856e-03 0.1242478 0.9211821 0.08890477
3.511192e-03 0.1250325 0.9204489 0.08960023
4.037017e-03 0.1260563 0.9194736 0.09051846
4.641589e-03 0.1273779 0.9181895 0.09171832
5.336699e-03 0.1290497 0.9165381 0.09323783
6.135907e-03 0.1311917 0.9143534 0.09516533
7.054802e-03 0.1337763 0.9116869 0.09744389
8.111308e-03 0.1369090 0.9083708 0.10017575
9.326033e-03 0.1403485 0.9047807 0.10315995
1.072267e-02 0.1442686 0.9006956 0.10650907
1.232847e-02 0.1491610 0.8953001 0.11072814
1.417474e-02 0.1546006 0.8892623 0.11535445
1.629751e-02 0.1598460 0.8840559 0.11952361
1.873817e-02 0.1659921 0.8778439 0.12436408
2.154435e-02 0.1736061 0.8694320 0.13042497
2.477076e-02 0.1830635 0.8576678 0.13811167
2.848036e-02 0.1936998 0.8436314 0.14679304
3.274549e-02 0.2049344 0.8287289 0.15594333
3.764936e-02 0.2172973 0.8115630 0.16606920
4.328761e-02 0.2310128 0.7912730 0.17738897
4.977024e-02 0.2477212 0.7609757 0.19110356
5.722368e-02 0.2674957 0.7164689 0.20734886
6.579332e-02 0.2889289 0.6589664 0.22517705
7.564633e-02 0.3086367 0.6018407 0.24138756
8.697490e-02 0.3233407 0.5674578 0.25346893
1.000000e-01 0.3380457 0.5345973 0.26560896
1.149757e-01 0.3519869 0.5106866 0.27729282
1.321941e-01 0.3667388 0.4921571 0.28996428
1.519911e-01 0.3835151 0.4705402 0.30453448
1.747528e-01 0.4025545 0.4493947 0.32115299
2.009233e-01 0.4247240 0.3409155 0.34036123
2.310130e-01 0.4397931 NaN 0.35168920
2.656088e-01 0.4397931 NaN 0.35168920
3.053856e-01 0.4397931 NaN 0.35168920
3.511192e-01 0.4397931 NaN 0.35168920
4.037017e-01 0.4397931 NaN 0.35168920
4.641589e-01 0.4397931 NaN 0.35168920
5.336699e-01 0.4397931 NaN 0.35168920
6.135907e-01 0.4397931 NaN 0.35168920
7.054802e-01 0.4397931 NaN 0.35168920
8.111308e-01 0.4397931 NaN 0.35168920
9.326033e-01 0.4397931 NaN 0.35168920
1.072267e+00 0.4397931 NaN 0.35168920
1.232847e+00 0.4397931 NaN 0.35168920
1.417474e+00 0.4397931 NaN 0.35168920
1.629751e+00 0.4397931 NaN 0.35168920
1.873817e+00 0.4397931 NaN 0.35168920
2.154435e+00 0.4397931 NaN 0.35168920
2.477076e+00 0.4397931 NaN 0.35168920
2.848036e+00 0.4397931 NaN 0.35168920
3.274549e+00 0.4397931 NaN 0.35168920
3.764936e+00 0.4397931 NaN 0.35168920
4.328761e+00 0.4397931 NaN 0.35168920
4.977024e+00 0.4397931 NaN 0.35168920
5.722368e+00 0.4397931 NaN 0.35168920
6.579332e+00 0.4397931 NaN 0.35168920
7.564633e+00 0.4397931 NaN 0.35168920
8.697490e+00 0.4397931 NaN 0.35168920
1.000000e+01 0.4397931 NaN 0.35168920
1.149757e+01 0.4397931 NaN 0.35168920
1.321941e+01 0.4397931 NaN 0.35168920
1.519911e+01 0.4397931 NaN 0.35168920
1.747528e+01 0.4397931 NaN 0.35168920
2.009233e+01 0.4397931 NaN 0.35168920
2.310130e+01 0.4397931 NaN 0.35168920
2.656088e+01 0.4397931 NaN 0.35168920
3.053856e+01 0.4397931 NaN 0.35168920
3.511192e+01 0.4397931 NaN 0.35168920
4.037017e+01 0.4397931 NaN 0.35168920
4.641589e+01 0.4397931 NaN 0.35168920
5.336699e+01 0.4397931 NaN 0.35168920
6.135907e+01 0.4397931 NaN 0.35168920
7.054802e+01 0.4397931 NaN 0.35168920
8.111308e+01 0.4397931 NaN 0.35168920
9.326033e+01 0.4397931 NaN 0.35168920
1.072267e+02 0.4397931 NaN 0.35168920
1.232847e+02 0.4397931 NaN 0.35168920
1.417474e+02 0.4397931 NaN 0.35168920
1.629751e+02 0.4397931 NaN 0.35168920
1.873817e+02 0.4397931 NaN 0.35168920
2.154435e+02 0.4397931 NaN 0.35168920
2.477076e+02 0.4397931 NaN 0.35168920
2.848036e+02 0.4397931 NaN 0.35168920
3.274549e+02 0.4397931 NaN 0.35168920
3.764936e+02 0.4397931 NaN 0.35168920
4.328761e+02 0.4397931 NaN 0.35168920
4.977024e+02 0.4397931 NaN 0.35168920
5.722368e+02 0.4397931 NaN 0.35168920
6.579332e+02 0.4397931 NaN 0.35168920
7.564633e+02 0.4397931 NaN 0.35168920
8.697490e+02 0.4397931 NaN 0.35168920
1.000000e+03 0.4397931 NaN 0.35168920
Tuning parameter 'alpha' was held constant at a value of 1
RMSE was used to select the optimal model using the smallest value.
The final values used for the model were alpha = 1 and lambda = 0.001.lassoLambda<-lassoModel$bestTune$lambda
lassoPredictor<- setdiff(names(carbonTrainData),"CarbonEmission")
lassoFinalModel<-glmnet(as.matrix(carbonTrainData[,lassoPredictor]),carbonTrainData[,"CarbonEmission"],alpha = 1,lambda = lassoLambda, family = "gaussian")Warning: NAs introduced by coercioncoeff<-coef(lassoFinalModel)
coeff27 x 1 sparse Matrix of class "dgCMatrix"
s0
(Intercept) 7.064707e+00
Body.Type .
Sex .
Diet .
How.Often.Shower .
Heating.Energy.Source .
Transport .
Vehicle.Type .
Social.Activity .
Monthly.Grocery.Bill 5.264763e-04
Frequency.of.Traveling.by.Air .
Vehicle.Monthly.Distance.Km 8.078680e-05
Waste.Bag.Size .
Waste.Bag.Weekly.Count 4.161160e-02
How.Long.TV.PC.Daily.Hour 1.146001e-03
How.Many.New.Clothes.Monthly 6.927597e-03
How.Long.Internet.Daily.Hour 3.612047e-03
Energy.efficiency .
Metal -6.556145e-02
Paper -7.009317e-02
Plastic -4.427840e-02
Glass -3.604447e-02
Stove 8.269632e-03
Oven 2.230270e-02
Microwave 3.326151e-04
Grill 1.333591e-02
Airfryer 3.105221e-15zeroCoeff<-coeff==0
zeroCoeff27 x 1 Matrix of class "lgeMatrix"
s0
(Intercept) FALSE
Body.Type TRUE
Sex TRUE
Diet TRUE
How.Often.Shower TRUE
Heating.Energy.Source TRUE
Transport TRUE
Vehicle.Type TRUE
Social.Activity TRUE
Monthly.Grocery.Bill FALSE
Frequency.of.Traveling.by.Air TRUE
Vehicle.Monthly.Distance.Km FALSE
Waste.Bag.Size TRUE
Waste.Bag.Weekly.Count FALSE
How.Long.TV.PC.Daily.Hour FALSE
How.Many.New.Clothes.Monthly FALSE
How.Long.Internet.Daily.Hour FALSE
Energy.efficiency TRUE
Metal FALSE
Paper FALSE
Plastic FALSE
Glass FALSE
Stove FALSE
Oven FALSE
Microwave FALSE
Grill FALSE
Airfryer FALSEplot(lassoModel)
#Ridge Model
set.seed(1)
ridgeModel<-train(CarbonEmission~.,data = carbonTrainData,method="glmnet",trControl= trainControl(method = "cv", number=5), tuneGrid = expand.grid(alpha=0, lambda=10^seq(-3,3,length=100))) Warning: There were missing values in resampled performance measures.ridgeModelglmnet
8001 samples
26 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6401, 6401, 6401, 6400, 6401
Resampling results across tuning parameters:
lambda RMSE Rsquared MAE
1.000000e-03 0.1241596 0.9222100 0.08865090
1.149757e-03 0.1241596 0.9222100 0.08865090
1.321941e-03 0.1241596 0.9222100 0.08865090
1.519911e-03 0.1241596 0.9222100 0.08865090
1.747528e-03 0.1241596 0.9222100 0.08865090
2.009233e-03 0.1241596 0.9222100 0.08865090
2.310130e-03 0.1241596 0.9222100 0.08865090
2.656088e-03 0.1241596 0.9222100 0.08865090
3.053856e-03 0.1241596 0.9222100 0.08865090
3.511192e-03 0.1241596 0.9222100 0.08865090
4.037017e-03 0.1241596 0.9222100 0.08865090
4.641589e-03 0.1241596 0.9222100 0.08865090
5.336699e-03 0.1241596 0.9222100 0.08865090
6.135907e-03 0.1241596 0.9222100 0.08865090
7.054802e-03 0.1241596 0.9222100 0.08865090
8.111308e-03 0.1241596 0.9222100 0.08865090
9.326033e-03 0.1241596 0.9222100 0.08865090
1.072267e-02 0.1241596 0.9222100 0.08865090
1.232847e-02 0.1241596 0.9222100 0.08865090
1.417474e-02 0.1241596 0.9222100 0.08865090
1.629751e-02 0.1241596 0.9222100 0.08865090
1.873817e-02 0.1241596 0.9222100 0.08865090
2.154435e-02 0.1241596 0.9222100 0.08865090
2.477076e-02 0.1245591 0.9220121 0.08897062
2.848036e-02 0.1253433 0.9216210 0.08960188
3.274549e-02 0.1263232 0.9211336 0.09040156
3.764936e-02 0.1275324 0.9205351 0.09139279
4.328761e-02 0.1290214 0.9197991 0.09261115
4.977024e-02 0.1308283 0.9189101 0.09408119
5.722368e-02 0.1330158 0.9178334 0.09587911
6.579332e-02 0.1356200 0.9165564 0.09804399
7.564633e-02 0.1387138 0.9150356 0.10063548
8.697490e-02 0.1423210 0.9132671 0.10365698
1.000000e-01 0.1465209 0.9111976 0.10718009
1.149757e-01 0.1513120 0.9088398 0.11124324
1.321941e-01 0.1567765 0.9061288 0.11590149
1.519911e-01 0.1628775 0.9030968 0.12112912
1.747528e-01 0.1696971 0.8996651 0.12698776
2.009233e-01 0.1771493 0.8958995 0.13340871
2.310130e-01 0.1853174 0.8916978 0.14044834
2.656088e-01 0.1940616 0.8871586 0.14797845
3.053856e-01 0.2034668 0.8821560 0.15602687
3.511192e-01 0.2133348 0.8768295 0.16444914
4.037017e-01 0.2237552 0.8710266 0.17329088
4.641589e-01 0.2344738 0.8649369 0.18233732
5.336699e-01 0.2455904 0.8583828 0.19165825
6.135907e-01 0.2568040 0.8516140 0.20102781
7.054802e-01 0.2682319 0.8444305 0.21057248
8.111308e-01 0.2795432 0.8371551 0.22002436
9.326033e-01 0.2908766 0.8295481 0.22946455
1.072267e+00 0.3018907 0.8219941 0.23860783
1.232847e+00 0.3127525 0.8142487 0.24762649
1.417474e+00 0.3231279 0.8067264 0.25623745
1.629751e+00 0.3332082 0.7991576 0.26457118
1.873817e+00 0.3426839 0.7919688 0.27238956
2.154435e+00 0.3517637 0.7848677 0.27986696
2.477076e+00 0.3601737 0.7782639 0.28677239
2.848036e+00 0.3681314 0.7718504 0.29329776
3.274549e+00 0.3754039 0.7659986 0.29926155
3.764936e+00 0.3822074 0.7603999 0.30482829
4.328761e+00 0.3883513 0.7553751 0.30984318
4.977024e+00 0.3940416 0.7506290 0.31448810
5.722368e+00 0.3991267 0.7464272 0.31863716
6.579332e+00 0.4037957 0.7424992 0.32244208
7.564633e+00 0.4079310 0.7390616 0.32580772
8.697490e+00 0.4117002 0.7358752 0.32887573
1.000000e+01 0.4150136 0.7331118 0.33157274
1.149757e+01 0.4180152 0.7305674 0.33401686
1.321941e+01 0.4206378 0.7283766 0.33615249
1.519911e+01 0.4230018 0.7263700 0.33807728
1.747528e+01 0.4250570 0.7246521 0.33974864
2.009233e+01 0.4269022 0.7230850 0.34124719
2.310130e+01 0.4285000 0.7217491 0.34254381
2.656088e+01 0.4299301 0.7205344 0.34370335
3.053856e+01 0.4311646 0.7195024 0.34470425
3.511192e+01 0.4322668 0.7185663 0.34559733
4.037017e+01 0.4332159 0.7177731 0.34636608
4.641589e+01 0.4340617 0.7170549 0.34705090
5.336699e+01 0.4347886 0.7164475 0.34763924
6.135907e+01 0.4354354 0.7158985 0.34816259
7.054802e+01 0.4359905 0.7154352 0.34861180
8.111308e+01 0.4364839 0.7150163 0.34901104
9.326033e+01 0.4369069 0.7146629 0.34935317
1.072267e+02 0.4372824 0.7143440 0.34965704
1.232847e+02 0.4376042 0.7140753 0.34991749
1.417474e+02 0.4378896 0.7138330 0.35014864
1.629751e+02 0.4381340 0.7136289 0.35034645
1.873817e+02 0.4383508 0.7134450 0.35052188
2.154435e+02 0.4390209 0.7133272 0.35106476
2.477076e+02 0.4397931 NaN 0.35168920
2.848036e+02 0.4397931 NaN 0.35168920
3.274549e+02 0.4397931 NaN 0.35168920
3.764936e+02 0.4397931 NaN 0.35168920
4.328761e+02 0.4397931 NaN 0.35168920
4.977024e+02 0.4397931 NaN 0.35168920
5.722368e+02 0.4397931 NaN 0.35168920
6.579332e+02 0.4397931 NaN 0.35168920
7.564633e+02 0.4397931 NaN 0.35168920
8.697490e+02 0.4397931 NaN 0.35168920
1.000000e+03 0.4397931 NaN 0.35168920
Tuning parameter 'alpha' was held constant at a value of 0
RMSE was used to select the optimal model using the smallest value.
The final values used for the model were alpha = 0 and lambda = 0.02154435.ridgeLambda<-ridgeModel$bestTune$lambda
ridgePredictor<- setdiff(names(carbonTrainData),"CarbonEmission")
ridgeFinalModel<-glmnet(as.matrix(carbonTrainData[,ridgePredictor]),carbonTrainData[,"CarbonEmission"],alpha = 1,lambda = ridgeLambda, family = "gaussian")Warning: NAs introduced by coercionridgeFinalModel
Call: glmnet(x = as.matrix(carbonTrainData[, ridgePredictor]), y = carbonTrainData[, "CarbonEmission"], family = "gaussian", alpha = 1, lambda = ridgeLambda)
Df %Dev Lambda
1 8 36.64 0.02154plot(ridgeModel)
set.seed(1)
enetModel<-train(CarbonEmission~., data = carbonTrainData, method = "glmnet", trControl=trainControl(method="cv",number=5,preProc="nzv"),tuneGrid=expand.grid(alpha=seq(0,1,length=10),lambda=10^seq(-3,1,length=100)))Warning: There were missing values in resampled performance measures.enetModelglmnet
8001 samples
26 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6401, 6401, 6401, 6400, 6401
Resampling results across tuning parameters:
alpha lambda RMSE Rsquared MAE
0.0000000 0.001000000 0.1241596 0.9222100 0.08865090
0.0000000 0.001097499 0.1241596 0.9222100 0.08865090
0.0000000 0.001204504 0.1241596 0.9222100 0.08865090
0.0000000 0.001321941 0.1241596 0.9222100 0.08865090
0.0000000 0.001450829 0.1241596 0.9222100 0.08865090
0.0000000 0.001592283 0.1241596 0.9222100 0.08865090
0.0000000 0.001747528 0.1241596 0.9222100 0.08865090
0.0000000 0.001917910 0.1241596 0.9222100 0.08865090
0.0000000 0.002104904 0.1241596 0.9222100 0.08865090
0.0000000 0.002310130 0.1241596 0.9222100 0.08865090
0.0000000 0.002535364 0.1241596 0.9222100 0.08865090
0.0000000 0.002782559 0.1241596 0.9222100 0.08865090
0.0000000 0.003053856 0.1241596 0.9222100 0.08865090
0.0000000 0.003351603 0.1241596 0.9222100 0.08865090
0.0000000 0.003678380 0.1241596 0.9222100 0.08865090
0.0000000 0.004037017 0.1241596 0.9222100 0.08865090
0.0000000 0.004430621 0.1241596 0.9222100 0.08865090
0.0000000 0.004862602 0.1241596 0.9222100 0.08865090
0.0000000 0.005336699 0.1241596 0.9222100 0.08865090
0.0000000 0.005857021 0.1241596 0.9222100 0.08865090
0.0000000 0.006428073 0.1241596 0.9222100 0.08865090
0.0000000 0.007054802 0.1241596 0.9222100 0.08865090
0.0000000 0.007742637 0.1241596 0.9222100 0.08865090
0.0000000 0.008497534 0.1241596 0.9222100 0.08865090
0.0000000 0.009326033 0.1241596 0.9222100 0.08865090
0.0000000 0.010235310 0.1241596 0.9222100 0.08865090
0.0000000 0.011233240 0.1241596 0.9222100 0.08865090
0.0000000 0.012328467 0.1241596 0.9222100 0.08865090
0.0000000 0.013530478 0.1241596 0.9222100 0.08865090
0.0000000 0.014849683 0.1241596 0.9222100 0.08865090
0.0000000 0.016297508 0.1241596 0.9222100 0.08865090
0.0000000 0.017886495 0.1241596 0.9222100 0.08865090
0.0000000 0.019630407 0.1241596 0.9222100 0.08865090
0.0000000 0.021544347 0.1241596 0.9222100 0.08865090
0.0000000 0.023644894 0.1243336 0.9221251 0.08878981
0.0000000 0.025950242 0.1248007 0.9218916 0.08916443
0.0000000 0.028480359 0.1253433 0.9216210 0.08960188
0.0000000 0.031257158 0.1259718 0.9213084 0.09011437
0.0000000 0.034304693 0.1266974 0.9209484 0.09070877
0.0000000 0.037649358 0.1275324 0.9205351 0.09139279
0.0000000 0.041320124 0.1284898 0.9200622 0.09217462
0.0000000 0.045348785 0.1295836 0.9195229 0.09307200
0.0000000 0.049770236 0.1308283 0.9189101 0.09408119
0.0000000 0.054622772 0.1322389 0.9182166 0.09523806
0.0000000 0.059948425 0.1338309 0.9174346 0.09655549
0.0000000 0.065793322 0.1356200 0.9165564 0.09804399
0.0000000 0.072208090 0.1376214 0.9155743 0.09971664
0.0000000 0.079248290 0.1398503 0.9144804 0.10158536
0.0000000 0.086974900 0.1423210 0.9132671 0.10365698
0.0000000 0.095454846 0.1450470 0.9119271 0.10593910
0.0000000 0.104761575 0.1480405 0.9104532 0.10846769
0.0000000 0.114975700 0.1513120 0.9088398 0.11124324
0.0000000 0.126185688 0.1548706 0.9070803 0.11427081
0.0000000 0.138488637 0.1587242 0.9051676 0.11756418
0.0000000 0.151991108 0.1628775 0.9030968 0.12112912
0.0000000 0.166810054 0.1673331 0.9008642 0.12496092
0.0000000 0.183073828 0.1720912 0.8984661 0.12905094
0.0000000 0.200923300 0.1771493 0.8958995 0.13340871
0.0000000 0.220513074 0.1825026 0.8931607 0.13802299
0.0000000 0.242012826 0.1881435 0.8902477 0.14288382
0.0000000 0.265608778 0.1940616 0.8871586 0.14797845
0.0000000 0.291505306 0.2002439 0.8838925 0.15327021
0.0000000 0.319926714 0.2066744 0.8804492 0.15876514
0.0000000 0.351119173 0.2133348 0.8768295 0.16444914
0.0000000 0.385352859 0.2202039 0.8730351 0.17028302
0.0000000 0.422924287 0.2272587 0.8690693 0.17625470
0.0000000 0.464158883 0.2344738 0.8649369 0.18233732
0.0000000 0.509413801 0.2418221 0.8606442 0.18850337
0.0000000 0.559081018 0.2492753 0.8561998 0.19473774
0.0000000 0.613590727 0.2568040 0.8516140 0.20102781
0.0000000 0.673415066 0.2643782 0.8468997 0.20735763
0.0000000 0.739072203 0.2719680 0.8420750 0.21369430
0.0000000 0.811130831 0.2795432 0.8371551 0.22002436
0.0000000 0.890215085 0.2870737 0.8321512 0.22630021
0.0000000 0.977009957 0.2945319 0.8270902 0.23250193
0.0000000 1.072267222 0.3018907 0.8219941 0.23860783
0.0000000 1.176811952 0.3091249 0.8168859 0.24461487
0.0000000 1.291549665 0.3162111 0.8117888 0.25049901
0.0000000 1.417474163 0.3231279 0.8067264 0.25623745
0.0000000 1.555676144 0.3298564 0.8017212 0.26180204
0.0000000 1.707352647 0.3363798 0.7967952 0.26719085
0.0000000 1.873817423 0.3426839 0.7919688 0.27238956
0.0000000 2.056512308 0.3487568 0.7872607 0.27739121
0.0000000 2.257019720 0.3545891 0.7826876 0.28218926
0.0000000 2.477076356 0.3601737 0.7782639 0.28677239
0.0000000 2.718588243 0.3655059 0.7740017 0.29114498
0.0000000 2.983647240 0.3705828 0.7699108 0.29530828
0.0000000 3.274549163 0.3754039 0.7659986 0.29926155
0.0000000 3.593813664 0.3799702 0.7622703 0.30299929
0.0000000 3.944206059 0.3842846 0.7587287 0.30652481
0.0000000 4.328761281 0.3883513 0.7553751 0.30984318
0.0000000 4.750810162 0.3921759 0.7522089 0.31296488
0.0000000 5.214008288 0.3957652 0.7492277 0.31589452
0.0000000 5.722367659 0.3991267 0.7464272 0.31863716
0.0000000 6.280291442 0.4022688 0.7438033 0.32119875
0.0000000 6.892612104 0.4052005 0.7413502 0.32358524
0.0000000 7.564633276 0.4079310 0.7390616 0.32580772
0.0000000 8.302175681 0.4104702 0.7369306 0.32787458
0.0000000 9.111627561 0.4128278 0.7349499 0.32979342
0.0000000 10.000000000 0.4150136 0.7331118 0.33157274
0.1111111 0.001000000 0.1217334 0.9234459 0.08677404
0.1111111 0.001097499 0.1217334 0.9234459 0.08677404
0.1111111 0.001204504 0.1217334 0.9234459 0.08677404
0.1111111 0.001321941 0.1217334 0.9234459 0.08677404
0.1111111 0.001450829 0.1217334 0.9234462 0.08677364
0.1111111 0.001592283 0.1217397 0.9234421 0.08677424
0.1111111 0.001747528 0.1217472 0.9234374 0.08677558
0.1111111 0.001917910 0.1217557 0.9234320 0.08677780
0.1111111 0.002104904 0.1217665 0.9234254 0.08678165
0.1111111 0.002310130 0.1217774 0.9234192 0.08678479
0.1111111 0.002535364 0.1217927 0.9234097 0.08679195
0.1111111 0.002782559 0.1218112 0.9233983 0.08680125
0.1111111 0.003053856 0.1218335 0.9233846 0.08681348
0.1111111 0.003351603 0.1218601 0.9233682 0.08682921
0.1111111 0.003678380 0.1218921 0.9233485 0.08684948
0.1111111 0.004037017 0.1219305 0.9233250 0.08687637
0.1111111 0.004430621 0.1219764 0.9232968 0.08690955
0.1111111 0.004862602 0.1220311 0.9232633 0.08694945
0.1111111 0.005336699 0.1220956 0.9232239 0.08699853
0.1111111 0.005857021 0.1221727 0.9231779 0.08705854
0.1111111 0.006428073 0.1222625 0.9231235 0.08712762
0.1111111 0.007054802 0.1223716 0.9230582 0.08721088
0.1111111 0.007742637 0.1225033 0.9229780 0.08731223
0.1111111 0.008497534 0.1226601 0.9228823 0.08743698
0.1111111 0.009326033 0.1228463 0.9227684 0.08758640
0.1111111 0.010235310 0.1230668 0.9226335 0.08776182
0.1111111 0.011233240 0.1233256 0.9224757 0.08796704
0.1111111 0.012328467 0.1236361 0.9222828 0.08821292
0.1111111 0.013530478 0.1239985 0.9220595 0.08850432
0.1111111 0.014849683 0.1244299 0.9217901 0.08885240
0.1111111 0.016297508 0.1249354 0.9214730 0.08927013
0.1111111 0.017886495 0.1255325 0.9210947 0.08977170
0.1111111 0.019630407 0.1262359 0.9206431 0.09036904
0.1111111 0.021544347 0.1270514 0.9201159 0.09106724
0.1111111 0.023644894 0.1280062 0.9194893 0.09188451
0.1111111 0.025950242 0.1291143 0.9187547 0.09282390
0.1111111 0.028480359 0.1303934 0.9178960 0.09391840
0.1111111 0.031257158 0.1318723 0.9168851 0.09519223
0.1111111 0.034304693 0.1335698 0.9157020 0.09664438
0.1111111 0.037649358 0.1355141 0.9143136 0.09831634
0.1111111 0.041320124 0.1377232 0.9127111 0.10017116
0.1111111 0.045348785 0.1402113 0.9108721 0.10224274
0.1111111 0.049770236 0.1430276 0.9087235 0.10459590
0.1111111 0.054622772 0.1461284 0.9063414 0.10718878
0.1111111 0.059948425 0.1494363 0.9038941 0.10994517
0.1111111 0.065793322 0.1529902 0.9013442 0.11292117
0.1111111 0.072208090 0.1567623 0.8987544 0.11610390
0.1111111 0.079248290 0.1608681 0.8959800 0.11954882
0.1111111 0.086974900 0.1653875 0.8928412 0.12334553
0.1111111 0.095454846 0.1703359 0.8893017 0.12748123
0.1111111 0.104761575 0.1756553 0.8854898 0.13194740
0.1111111 0.114975700 0.1812419 0.8816911 0.13661277
0.1111111 0.126185688 0.1872992 0.8774701 0.14171717
0.1111111 0.138488637 0.1938499 0.8727256 0.14722242
0.1111111 0.151991108 0.2009144 0.8673238 0.15314502
0.1111111 0.166810054 0.2084710 0.8611874 0.15946882
0.1111111 0.183073828 0.2165904 0.8540000 0.16627763
0.1111111 0.200923300 0.2253002 0.8454287 0.17358788
0.1111111 0.220513074 0.2344942 0.8355983 0.18126942
0.1111111 0.242012826 0.2438593 0.8255182 0.18905006
0.1111111 0.265608778 0.2535111 0.8147922 0.19704125
0.1111111 0.291505306 0.2634364 0.8032475 0.20522311
0.1111111 0.319926714 0.2737713 0.7898724 0.21375087
0.1111111 0.351119173 0.2842888 0.7753295 0.22246859
0.1111111 0.385352859 0.2949711 0.7595295 0.23134406
0.1111111 0.422924287 0.3060137 0.7405776 0.24047033
0.1111111 0.464158883 0.3174432 0.7170064 0.24990101
0.1111111 0.509413801 0.3291159 0.6882222 0.25951702
0.1111111 0.559081018 0.3405148 0.6571255 0.26895359
0.1111111 0.613590727 0.3517377 0.6222571 0.27825887
0.1111111 0.673415066 0.3618434 0.5940626 0.28668262
0.1111111 0.739072203 0.3710416 0.5715066 0.29438372
0.1111111 0.811130831 0.3793860 0.5563613 0.30128810
0.1111111 0.890215085 0.3874181 0.5409958 0.30792701
0.1111111 0.977009957 0.3950869 0.5281318 0.31427023
0.1111111 1.072267222 0.4024619 0.5182535 0.32037846
0.1111111 1.176811952 0.4097467 0.5038059 0.32646378
0.1111111 1.291549665 0.4165295 0.4890961 0.33224260
0.1111111 1.417474163 0.4231500 0.4611175 0.33789654
0.1111111 1.555676144 0.4288538 0.4430035 0.34277159
0.1111111 1.707352647 0.4341464 0.4012099 0.34727384
0.1111111 1.873817423 0.4377089 0.2710946 0.35022545
0.1111111 2.056512308 0.4397661 0.2502579 0.35167019
0.1111111 2.257019720 0.4397931 NaN 0.35168920
0.1111111 2.477076356 0.4397931 NaN 0.35168920
0.1111111 2.718588243 0.4397931 NaN 0.35168920
0.1111111 2.983647240 0.4397931 NaN 0.35168920
0.1111111 3.274549163 0.4397931 NaN 0.35168920
0.1111111 3.593813664 0.4397931 NaN 0.35168920
0.1111111 3.944206059 0.4397931 NaN 0.35168920
0.1111111 4.328761281 0.4397931 NaN 0.35168920
0.1111111 4.750810162 0.4397931 NaN 0.35168920
0.1111111 5.214008288 0.4397931 NaN 0.35168920
0.1111111 5.722367659 0.4397931 NaN 0.35168920
0.1111111 6.280291442 0.4397931 NaN 0.35168920
0.1111111 6.892612104 0.4397931 NaN 0.35168920
0.1111111 7.564633276 0.4397931 NaN 0.35168920
0.1111111 8.302175681 0.4397931 NaN 0.35168920
0.1111111 9.111627561 0.4397931 NaN 0.35168920
0.1111111 10.000000000 0.4397931 NaN 0.35168920
[ reached getOption("max.print") -- omitted 800 rows ]
RMSE was used to select the optimal model using the smallest value.
The final values used for the model were alpha = 0.1111111 and lambda = 0.001321941.enetModel$bestTuneenetLambda<-enetModel$bestTune$lambda
enetAlpha<-enetModel$bestTune$alpha
enetPredector<-setdiff(names(carbonTrainData),"CarbonEmission")
enetFinalModel<-glmnet(as.matrix(carbonTrainData[,enetPredector]),carbonTrainData[,"CarbonEmission"], alpha = enetAlpha,lambda = enetLambda, family = "gaussian")Warning: NAs introduced by coercionenetFinalModel
Call: glmnet(x = as.matrix(carbonTrainData[, enetPredector]), y = carbonTrainData[, "CarbonEmission"], family = "gaussian", alpha = enetAlpha, lambda = enetLambda)
Df %Dev Lambda
1 15 38.87 0.001322#Random Forest Model
library(randomForest)randomForest 4.7-1.1
Type rfNews() to see new features/changes/bug fixes.
Attaching package: ‘randomForest’
The following object is masked from ‘package:dplyr’:
combine
The following object is masked from ‘package:ggplot2’:
marginset.seed(1)
randomForestModel<-randomForest(CarbonEmission~.,data = carbonTrainData)
randomForestModel
Call:
randomForest(formula = CarbonEmission ~ ., data = carbonTrainData)
Type of random forest: regression
Number of trees: 500
No. of variables tried at each split: 8
Mean of squared residuals: 0.01633518
% Var explained: 91.56mRf<-train(CarbonEmission~.,
data=carbonTrainData,
method="rf",
trControl=trainControl(method = "cv", number =5)
)mRfRandom Forest
8001 samples
26 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6401, 6401, 6401, 6400, 6401
Resampling results across tuning parameters:
mtry RMSE Rsquared MAE
2 0.2675949 0.8091212 0.2085785
23 0.1383621 0.9051976 0.1068202
45 0.1421811 0.8973471 0.1099139
RMSE was used to select the optimal model using the smallest value.
The final value used for the model was mtry = 23.varImp(mRf)rf variable importance
only 20 most important variables shown (out of 45)rfPred<-predict(mRf,newdata = carbonTestData)
MAE(carbonTestData$CarbonEmission,rfPred)[1] 0.1045264rmse(carbonTestData$CarbonEmission,rfPred)[1] 0.00120078cor(carbonTestData$CarbonEmission,rfPred)^2[1] 0.9072645plot(carbonTestData$CarbonEmission,rfPred)
GBM
set.seed(1)
grBoostedTree<-train(
CarbonEmission~.,
data = carbonTrainData,
method="gbm",
trControl=trainControl(method = "cv",number = 5)
)Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1840 nan 0.1000 0.0083
2 0.1764 nan 0.1000 0.0078
3 0.1696 nan 0.1000 0.0067
4 0.1630 nan 0.1000 0.0063
5 0.1574 nan 0.1000 0.0058
6 0.1521 nan 0.1000 0.0052
7 0.1475 nan 0.1000 0.0047
8 0.1434 nan 0.1000 0.0039
9 0.1392 nan 0.1000 0.0041
10 0.1357 nan 0.1000 0.0032
20 0.1098 nan 0.1000 0.0017
40 0.0855 nan 0.1000 0.0009
60 0.0696 nan 0.1000 0.0006
80 0.0584 nan 0.1000 0.0005
100 0.0502 nan 0.1000 0.0004
120 0.0441 nan 0.1000 0.0002
140 0.0395 nan 0.1000 0.0002
150 0.0375 nan 0.1000 0.0002
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1765 nan 0.1000 0.0158
2 0.1638 nan 0.1000 0.0126
3 0.1535 nan 0.1000 0.0102
4 0.1447 nan 0.1000 0.0082
5 0.1374 nan 0.1000 0.0074
6 0.1313 nan 0.1000 0.0060
7 0.1249 nan 0.1000 0.0061
8 0.1198 nan 0.1000 0.0053
9 0.1148 nan 0.1000 0.0049
10 0.1105 nan 0.1000 0.0043
20 0.0804 nan 0.1000 0.0028
40 0.0534 nan 0.1000 0.0009
60 0.0394 nan 0.1000 0.0004
80 0.0308 nan 0.1000 0.0003
100 0.0256 nan 0.1000 0.0002
120 0.0215 nan 0.1000 0.0001
140 0.0187 nan 0.1000 0.0001
150 0.0176 nan 0.1000 0.0001
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1740 nan 0.1000 0.0187
2 0.1583 nan 0.1000 0.0156
3 0.1457 nan 0.1000 0.0125
4 0.1349 nan 0.1000 0.0105
5 0.1264 nan 0.1000 0.0083
6 0.1184 nan 0.1000 0.0079
7 0.1119 nan 0.1000 0.0062
8 0.1057 nan 0.1000 0.0059
9 0.1008 nan 0.1000 0.0049
10 0.0961 nan 0.1000 0.0044
20 0.0673 nan 0.1000 0.0019
40 0.0412 nan 0.1000 0.0010
60 0.0290 nan 0.1000 0.0004
80 0.0224 nan 0.1000 0.0003
100 0.0179 nan 0.1000 0.0001
120 0.0146 nan 0.1000 0.0001
140 0.0125 nan 0.1000 0.0001
150 0.0116 nan 0.1000 0.0001
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1856 nan 0.1000 0.0089
2 0.1777 nan 0.1000 0.0076
3 0.1703 nan 0.1000 0.0073
4 0.1641 nan 0.1000 0.0062
5 0.1583 nan 0.1000 0.0058
6 0.1533 nan 0.1000 0.0048
7 0.1482 nan 0.1000 0.0050
8 0.1441 nan 0.1000 0.0040
9 0.1404 nan 0.1000 0.0037
10 0.1366 nan 0.1000 0.0039
20 0.1116 nan 0.1000 0.0017
40 0.0869 nan 0.1000 0.0008
60 0.0712 nan 0.1000 0.0006
80 0.0598 nan 0.1000 0.0004
100 0.0514 nan 0.1000 0.0003
120 0.0451 nan 0.1000 0.0002
140 0.0405 nan 0.1000 0.0002
150 0.0384 nan 0.1000 0.0001
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1785 nan 0.1000 0.0156
2 0.1654 nan 0.1000 0.0126
3 0.1548 nan 0.1000 0.0105
4 0.1459 nan 0.1000 0.0086
5 0.1386 nan 0.1000 0.0071
6 0.1323 nan 0.1000 0.0060
7 0.1271 nan 0.1000 0.0052
8 0.1219 nan 0.1000 0.0050
9 0.1161 nan 0.1000 0.0058
10 0.1117 nan 0.1000 0.0043
20 0.0824 nan 0.1000 0.0016
40 0.0548 nan 0.1000 0.0007
60 0.0400 nan 0.1000 0.0005
80 0.0308 nan 0.1000 0.0004
100 0.0254 nan 0.1000 0.0002
120 0.0213 nan 0.1000 0.0001
140 0.0185 nan 0.1000 0.0001
150 0.0174 nan 0.1000 0.0001
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1760 nan 0.1000 0.0187
2 0.1605 nan 0.1000 0.0159
3 0.1476 nan 0.1000 0.0124
4 0.1376 nan 0.1000 0.0102
5 0.1286 nan 0.1000 0.0088
6 0.1201 nan 0.1000 0.0081
7 0.1133 nan 0.1000 0.0067
8 0.1070 nan 0.1000 0.0064
9 0.1019 nan 0.1000 0.0050
10 0.0971 nan 0.1000 0.0048
20 0.0689 nan 0.1000 0.0024
40 0.0422 nan 0.1000 0.0007
60 0.0300 nan 0.1000 0.0006
80 0.0227 nan 0.1000 0.0002
100 0.0181 nan 0.1000 0.0002
120 0.0148 nan 0.1000 0.0001
140 0.0126 nan 0.1000 0.0001
150 0.0117 nan 0.1000 0.0000
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1849 nan 0.1000 0.0087
2 0.1772 nan 0.1000 0.0079
3 0.1697 nan 0.1000 0.0072
4 0.1635 nan 0.1000 0.0062
5 0.1574 nan 0.1000 0.0058
6 0.1524 nan 0.1000 0.0046
7 0.1472 nan 0.1000 0.0051
8 0.1430 nan 0.1000 0.0040
9 0.1389 nan 0.1000 0.0043
10 0.1352 nan 0.1000 0.0035
20 0.1105 nan 0.1000 0.0016
40 0.0859 nan 0.1000 0.0010
60 0.0702 nan 0.1000 0.0006
80 0.0586 nan 0.1000 0.0004
100 0.0504 nan 0.1000 0.0003
120 0.0440 nan 0.1000 0.0002
140 0.0394 nan 0.1000 0.0001
150 0.0374 nan 0.1000 0.0001
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1777 nan 0.1000 0.0156
2 0.1646 nan 0.1000 0.0131
3 0.1540 nan 0.1000 0.0103
4 0.1448 nan 0.1000 0.0089
5 0.1373 nan 0.1000 0.0073
6 0.1309 nan 0.1000 0.0061
7 0.1245 nan 0.1000 0.0064
8 0.1194 nan 0.1000 0.0049
9 0.1142 nan 0.1000 0.0053
10 0.1102 nan 0.1000 0.0040
20 0.0812 nan 0.1000 0.0018
40 0.0536 nan 0.1000 0.0010
60 0.0393 nan 0.1000 0.0005
80 0.0308 nan 0.1000 0.0004
100 0.0252 nan 0.1000 0.0001
120 0.0213 nan 0.1000 0.0002
140 0.0186 nan 0.1000 0.0001
150 0.0175 nan 0.1000 0.0001
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1741 nan 0.1000 0.0187
2 0.1585 nan 0.1000 0.0155
3 0.1461 nan 0.1000 0.0126
4 0.1357 nan 0.1000 0.0103
5 0.1255 nan 0.1000 0.0100
6 0.1181 nan 0.1000 0.0075
7 0.1111 nan 0.1000 0.0071
8 0.1053 nan 0.1000 0.0056
9 0.1002 nan 0.1000 0.0050
10 0.0958 nan 0.1000 0.0045
20 0.0675 nan 0.1000 0.0020
40 0.0421 nan 0.1000 0.0007
60 0.0296 nan 0.1000 0.0003
80 0.0223 nan 0.1000 0.0003
100 0.0181 nan 0.1000 0.0001
120 0.0150 nan 0.1000 0.0001
140 0.0126 nan 0.1000 0.0001
150 0.0114 nan 0.1000 0.0001
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1851 nan 0.1000 0.0086
2 0.1775 nan 0.1000 0.0078
3 0.1701 nan 0.1000 0.0073
4 0.1635 nan 0.1000 0.0064
5 0.1578 nan 0.1000 0.0057
6 0.1527 nan 0.1000 0.0049
7 0.1476 nan 0.1000 0.0049
8 0.1436 nan 0.1000 0.0039
9 0.1396 nan 0.1000 0.0039
10 0.1356 nan 0.1000 0.0038
20 0.1116 nan 0.1000 0.0018
40 0.0869 nan 0.1000 0.0009
60 0.0711 nan 0.1000 0.0005
80 0.0596 nan 0.1000 0.0005
100 0.0513 nan 0.1000 0.0003
120 0.0452 nan 0.1000 0.0002
140 0.0404 nan 0.1000 0.0001
150 0.0384 nan 0.1000 0.0002
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1785 nan 0.1000 0.0155
2 0.1654 nan 0.1000 0.0128
3 0.1552 nan 0.1000 0.0102
4 0.1464 nan 0.1000 0.0088
5 0.1388 nan 0.1000 0.0076
6 0.1324 nan 0.1000 0.0063
7 0.1262 nan 0.1000 0.0064
8 0.1208 nan 0.1000 0.0052
9 0.1156 nan 0.1000 0.0053
10 0.1110 nan 0.1000 0.0045
20 0.0824 nan 0.1000 0.0016
40 0.0554 nan 0.1000 0.0008
60 0.0406 nan 0.1000 0.0005
80 0.0321 nan 0.1000 0.0002
100 0.0265 nan 0.1000 0.0003
120 0.0222 nan 0.1000 0.0002
140 0.0193 nan 0.1000 0.0001
150 0.0180 nan 0.1000 0.0001
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1748 nan 0.1000 0.0191
2 0.1595 nan 0.1000 0.0156
3 0.1467 nan 0.1000 0.0124
4 0.1366 nan 0.1000 0.0097
5 0.1272 nan 0.1000 0.0092
6 0.1187 nan 0.1000 0.0084
7 0.1122 nan 0.1000 0.0063
8 0.1061 nan 0.1000 0.0061
9 0.1011 nan 0.1000 0.0050
10 0.0968 nan 0.1000 0.0040
20 0.0688 nan 0.1000 0.0018
40 0.0424 nan 0.1000 0.0007
60 0.0301 nan 0.1000 0.0003
80 0.0229 nan 0.1000 0.0002
100 0.0185 nan 0.1000 0.0002
120 0.0151 nan 0.1000 0.0002
140 0.0129 nan 0.1000 0.0001
150 0.0118 nan 0.1000 0.0001
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1834 nan 0.1000 0.0087
2 0.1759 nan 0.1000 0.0075
3 0.1685 nan 0.1000 0.0070
4 0.1627 nan 0.1000 0.0056
5 0.1568 nan 0.1000 0.0062
6 0.1518 nan 0.1000 0.0048
7 0.1467 nan 0.1000 0.0049
8 0.1426 nan 0.1000 0.0039
9 0.1384 nan 0.1000 0.0042
10 0.1348 nan 0.1000 0.0038
20 0.1101 nan 0.1000 0.0018
40 0.0860 nan 0.1000 0.0010
60 0.0703 nan 0.1000 0.0006
80 0.0589 nan 0.1000 0.0004
100 0.0506 nan 0.1000 0.0003
120 0.0445 nan 0.1000 0.0002
140 0.0398 nan 0.1000 0.0002
150 0.0379 nan 0.1000 0.0002
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1763 nan 0.1000 0.0158
2 0.1632 nan 0.1000 0.0127
3 0.1532 nan 0.1000 0.0101
4 0.1441 nan 0.1000 0.0085
5 0.1368 nan 0.1000 0.0071
6 0.1307 nan 0.1000 0.0063
7 0.1249 nan 0.1000 0.0058
8 0.1195 nan 0.1000 0.0055
9 0.1148 nan 0.1000 0.0046
10 0.1102 nan 0.1000 0.0045
20 0.0808 nan 0.1000 0.0018
40 0.0540 nan 0.1000 0.0009
60 0.0400 nan 0.1000 0.0004
80 0.0315 nan 0.1000 0.0003
100 0.0258 nan 0.1000 0.0002
120 0.0217 nan 0.1000 0.0001
140 0.0189 nan 0.1000 0.0001
150 0.0178 nan 0.1000 0.0001
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1734 nan 0.1000 0.0186
2 0.1582 nan 0.1000 0.0152
3 0.1455 nan 0.1000 0.0127
4 0.1352 nan 0.1000 0.0103
5 0.1257 nan 0.1000 0.0094
6 0.1173 nan 0.1000 0.0082
7 0.1109 nan 0.1000 0.0063
8 0.1051 nan 0.1000 0.0056
9 0.1000 nan 0.1000 0.0052
10 0.0952 nan 0.1000 0.0047
20 0.0680 nan 0.1000 0.0018
40 0.0416 nan 0.1000 0.0009
60 0.0290 nan 0.1000 0.0004
80 0.0218 nan 0.1000 0.0002
100 0.0174 nan 0.1000 0.0001
120 0.0141 nan 0.1000 0.0003
140 0.0120 nan 0.1000 0.0001
150 0.0111 nan 0.1000 0.0000
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1743 nan 0.1000 0.0190
2 0.1589 nan 0.1000 0.0156
3 0.1461 nan 0.1000 0.0125
4 0.1359 nan 0.1000 0.0102
5 0.1262 nan 0.1000 0.0095
6 0.1189 nan 0.1000 0.0073
7 0.1115 nan 0.1000 0.0073
8 0.1056 nan 0.1000 0.0056
9 0.1005 nan 0.1000 0.0051
10 0.0959 nan 0.1000 0.0046
20 0.0678 nan 0.1000 0.0018
40 0.0417 nan 0.1000 0.0007
60 0.0294 nan 0.1000 0.0003
80 0.0224 nan 0.1000 0.0003
100 0.0179 nan 0.1000 0.0001
120 0.0148 nan 0.1000 0.0001
140 0.0125 nan 0.1000 0.0000
150 0.0116 nan 0.1000 0.0001grBoostedTreeStochastic Gradient Boosting
8001 samples
26 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6401, 6401, 6401, 6400, 6401
Resampling results across tuning parameters:
interaction.depth n.trees RMSE Rsquared MAE
1 50 0.2799859 0.6817099 0.21734673
1 100 0.2268772 0.7921416 0.17361732
1 150 0.1962693 0.8366385 0.14863782
2 50 0.2171188 0.8190082 0.16598681
2 100 0.1638148 0.8868431 0.12368291
2 150 0.1371742 0.9154801 0.10233794
3 50 0.1894213 0.8550887 0.14434186
3 100 0.1391537 0.9160350 0.10460569
3 150 0.1130848 0.9406167 0.08385162
Tuning parameter 'shrinkage' was held constant at a value of 0.1
Tuning parameter 'n.minobsinnode' was held constant at a value of 10
RMSE was used to select the optimal model using the smallest value.
The final values used for the model were n.trees = 150, interaction.depth = 3, shrinkage = 0.1 and n.minobsinnode = 10.gbmPred<-predict(grBoostedTree, carbonTestData)MAE(carbonTestData$CarbonEmission,gbmPred)[1] 0.08074864rmse(carbonTestData$CarbonEmission,gbmPred)[1] 0.0002656894cor(carbonTestData$CarbonEmission,gbmPred)^2[1] 0.9431872plot(carbonTestData$CarbonEmission,gbmPred)
#SV Linear Model
set.seed(1)
svmLinear<-train(
CarbonEmission~.,
data = carbonTrainData,
method="svmLinear",
trControl=trainControl(method = "cv",number = 5, preProc=c("center","scale"))
)svmLinearSupport Vector Machines with Linear Kernel
8001 samples
26 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6401, 6401, 6401, 6400, 6401
Resampling results:
RMSE Rsquared MAE
0.1223375 0.9230945 0.08612926
Tuning parameter 'C' was held constant at a value of 1svmPred<-predict(svmLinear,carbonTestData)
plot(svmPred,carbonTestData$CarbonEmission)
#SVM Radial Model
set.seed(1)
svmRadial<-train(
CarbonEmission~.,
data = carbonTrainData,
method="svmRadial",
trControl=trainControl(method = "cv",number = 5, preProc=c("center","scale"))
)svmRadialSupport Vector Machines with Radial Basis Function Kernel
8001 samples
26 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6401, 6401, 6401, 6400, 6401
Resampling results across tuning parameters:
C RMSE Rsquared MAE
0.25 0.08070183 0.9684766 0.05543867
0.50 0.06813583 0.9769891 0.04773321
1.00 0.06065717 0.9813954 0.04355294
Tuning parameter 'sigma' was held constant at a value of 0.01193687
RMSE was used to select the optimal model using the smallest value.
The final values used for the model were sigma = 0.01193687 and C = 1.svmRadialPred<-predict(svmRadial,carbonTestData)
plot(svmRadialPred,carbonTestData$CarbonEmission)
#Comparing models
compare=resamples(list(KNN=knnModel,LIN=lmModel,stepWise=stepwiseModel,Lasso=lassoModel,Ridge=ridgeModel,Enet=enetModel,RF=mRf,GBM=grBoostedTree,SVML=svmLinear,SVMR=svmRadial))
summary(compare) # Out of all the models SVM Radial stands out the most
Call:
summary.resamples(object = compare)
Models: KNN, LIN, stepWise, Lasso, Ridge, Enet, RF, GBM, SVML, SVMR
Number of resamples: 5
MAE
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
KNN 0.29355718 0.30011897 0.30358891 0.30104742 0.30359972 0.30437235 0
LIN 0.08468189 0.08653726 0.08668226 0.08674322 0.08771081 0.08810388 0
stepWise 0.27076896 0.27236187 0.27267260 0.27275357 0.27295504 0.27500935 0
Lasso 0.08263733 0.08452922 0.08689541 0.08693488 0.08991372 0.09069875 0
Ridge 0.08461479 0.08660820 0.08863896 0.08865090 0.09132776 0.09206482 0
Enet 0.08278867 0.08412890 0.08658334 0.08677404 0.08993674 0.09043257 0
RF 0.10408932 0.10538108 0.10750605 0.10682021 0.10836087 0.10876372 0
GBM 0.07970661 0.08311406 0.08431983 0.08385162 0.08521232 0.08690528 0
SVML 0.08208710 0.08349344 0.08672664 0.08612926 0.08894605 0.08939306 0
SVMR 0.04203058 0.04268964 0.04371276 0.04355294 0.04405078 0.04528092 0
RMSE
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
KNN 0.37295011 0.37736743 0.37851215 0.37897650 0.37913018 0.3869226 0
LIN 0.11743009 0.12058876 0.12318123 0.12169555 0.12340748 0.1238702 0
stepWise 0.34358303 0.34484586 0.34496809 0.34607902 0.34713797 0.3498602 0
Lasso 0.11662100 0.11703960 0.12364901 0.12198581 0.12624277 0.1263767 0
Ridge 0.11907983 0.11994269 0.12566449 0.12415964 0.12788642 0.1282248 0
Enet 0.11604668 0.11694091 0.12328642 0.12173339 0.12617744 0.1262155 0
RF 0.13408157 0.13614576 0.13894945 0.13836211 0.14047841 0.1421553 0
GBM 0.10685459 0.11180062 0.11338918 0.11308477 0.11588839 0.1174911 0
SVML 0.11628786 0.11705824 0.12519442 0.12233751 0.12626695 0.1268801 0
SVMR 0.05776811 0.05964871 0.06149819 0.06065717 0.06175535 0.0626155 0
Rsquared
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
KNN 0.2543177 0.2631183 0.2650880 0.2674213 0.2767798 0.2778026 0
LIN 0.9185705 0.9216487 0.9246806 0.9235284 0.9253275 0.9274147 0
stepWise 0.3614847 0.3715791 0.3780496 0.3807424 0.3938578 0.3987405 0
Lasso 0.9170290 0.9192558 0.9230649 0.9232209 0.9279729 0.9287817 0
Ridge 0.9155089 0.9187241 0.9226270 0.9222100 0.9266525 0.9275377 0
Enet 0.9173033 0.9192842 0.9233576 0.9234459 0.9285877 0.9286968 0
RF 0.9002318 0.9022657 0.9053106 0.9051976 0.9090824 0.9090975 0
GBM 0.9376708 0.9388972 0.9390649 0.9406167 0.9404415 0.9470088 0
SVML 0.9174402 0.9185159 0.9219757 0.9230945 0.9287006 0.9288401 0
SVMR 0.9805681 0.9808349 0.9812240 0.9813954 0.9815897 0.9827600 0Neural Network Preprocessing
library(caret)
carbonInd<-createDataPartition(carbonTrainData$CarbonEmission,p=0.9,list = FALSE)
carbonIndex<-which(names(carbonTrainData)=='CarbonEmission')
carbonTrainingData<-carbonTrainData[carbonInd,-carbonIndex]
str(carbonTrainingData)'data.frame': 7202 obs. of 26 variables:
$ Body.Type : Factor w/ 4 levels "normal","obese",..: 2 3 2 3 3 4 1 2 4 4 ...
$ Sex : Factor w/ 2 levels "female","male": 1 2 1 2 2 1 1 2 1 1 ...
$ Diet : Factor w/ 4 levels "omnivore","pescatarian",..: 4 1 4 4 1 2 4 4 1 3 ...
$ How.Often.Shower : Factor w/ 4 levels "daily","less frequently",..: 2 3 1 2 1 1 3 3 4 2 ...
$ Heating.Energy.Source : Factor w/ 4 levels "coal","electricity",..: 3 4 1 4 4 4 4 1 1 2 ...
$ Transport : Factor w/ 3 levels "private","public",..: 3 1 1 2 2 2 2 3 3 1 ...
$ Vehicle.Type : Factor w/ 6 levels "diesel","electric",..: 3 6 1 3 3 3 3 3 3 5 ...
$ Social.Activity : Factor w/ 3 levels "never","often",..: 2 1 2 3 1 2 1 1 2 3 ...
$ Monthly.Grocery.Bill : num 114 138 266 144 200 135 146 111 114 111 ...
$ Frequency.of.Traveling.by.Air: Factor w/ 4 levels "frequently","never",..: 3 2 4 1 1 3 2 4 3 3 ...
$ Vehicle.Monthly.Distance.Km : num 9 2472 8457 658 1376 ...
$ Waste.Bag.Size : Factor w/ 4 levels "extra large",..: 1 4 2 2 3 1 1 3 2 2 ...
$ Waste.Bag.Weekly.Count : num 3 1 1 1 3 1 4 5 3 6 ...
$ How.Long.TV.PC.Daily.Hour : num 9 14 3 22 3 8 12 9 18 13 ...
$ How.Many.New.Clothes.Monthly : num 38 47 5 18 31 23 27 4 27 16 ...
$ How.Long.Internet.Daily.Hour : num 5 6 6 9 15 18 21 4 4 10 ...
$ Energy.efficiency : Factor w/ 3 levels "No","Sometimes",..: 1 2 3 2 3 2 1 2 3 2 ...
$ Metal : num 1 1 0 1 0 0 0 0 0 1 ...
$ Paper : num 0 0 1 1 0 0 1 0 0 0 ...
$ Plastic : num 0 0 0 0 0 0 1 0 1 1 ...
$ Glass : num 0 0 0 1 1 1 0 0 0 1 ...
$ Stove : num 1 0 0 1 0 0 1 1 1 1 ...
$ Oven : num 0 1 1 1 0 0 0 1 0 1 ...
$ Microwave : num 1 1 0 1 1 1 1 1 0 1 ...
$ Grill : num 0 0 0 0 1 1 0 0 0 1 ...
$ Airfryer : num 0 0 0 0 1 1 0 0 0 1 ...carbonTrainingLabels<-carbonTrainData[carbonInd,carbonIndex]
str(carbonTrainingLabels) num [1:7202] 7.55 7.86 8.46 7.41 7.82 ...carbonValidationData<-carbonTrainData[-carbonInd,-carbonIndex]
carbonValidationData
carbonValidationLabels<-carbonTrainData[-carbonInd,carbonIndex]
str(carbonValidationLabels) num [1:799] 7.71 7.75 7.09 7.41 7.51 ...carbonTestingData<-carbonTestData[,-carbonIndex]
carbonTestingData
carbonTestingLabels<-carbonTestData[,carbonIndex]
str(carbonTestingLabels) num [1:1999] 6.98 7.51 7.11 7.31 7.48 ...dim(carbonTrainingData)[1] 7202 26dim(carbonTestingData)[1] 1999 26#Scaling numeric Variables and one hot encoding categorical variables
library(mltools)
Attaching package: ‘mltools’
The following object is masked _by_ ‘.GlobalEnv’:
rmse
The following object is masked from ‘package:tidyr’:
replace_nalibrary(data.table)data.table 1.15.4 using 1 threads (see ?getDTthreads). Latest news: r-datatable.com
**********
This installation of data.table has not detected OpenMP support. It should still work but in single-threaded mode.
This is a Mac. Please read https://mac.r-project.org/openmp/. Please engage with Apple and ask them for support. Check r-datatable.com for updates, and our Mac instructions here: https://github.com/Rdatatable/data.table/wiki/Installation. After several years of many reports of installation problems on Mac, it's time to gingerly point out that there have been no similar problems on Windows or Linux.
**********
Attaching package: ‘data.table’
The following objects are masked from ‘package:lubridate’:
hour, isoweek, mday, minute, month, quarter, second, wday, week, yday, year
The following objects are masked from ‘package:dplyr’:
between, first, last
The following object is masked from ‘package:purrr’:
transposenumericCols<-c("Monthly.Grocery.Bill","Vehicle.Monthly.Distance.Km","Waste.Bag.Weekly.Count",
"How.Long.TV.PC.Daily.Hour","How.Many.New.Clothes.Monthly","How.Long.Internet.Daily.Hour","Metal","Paper","Plastic","Glass","Stove","Oven"
,"Microwave","Grill","Airfryer")
categoricalCols<-c("Body.Type","Sex","Diet","How.Often.Shower","Heating.Energy.Source","Transport","Vehicle.Type","Social.Activity",
"Frequency.of.Traveling.by.Air","Waste.Bag.Size","Energy.efficiency")
carbonTrainingDataNew<-scale(carbonTrainingData[,numericCols])
colMeanTrain<-attr(carbonTrainingDataNew,"scaled:center")
colStddevsTrain<-attr(carbonTrainingDataNew,"scaled:scale")
carbonTrainingData[,numericCols]<-carbonTrainingDataNew
carbonValidationData[,numericCols]<-scale(carbonValidationData[,numericCols],center = colMeanTrain,scale = colStddevsTrain)
carbonTestingData[,numericCols]<-scale(carbonTestingData[,numericCols],center = colMeanTrain,scale = colStddevsTrain)
carbonTrainingTable<-as.data.table(carbonTrainingData)
carbonValidationTable<-as.data.table(carbonValidationData)
carbonTestingTable<-as.data.table(carbonTestingData)
carbonTrainingOneHot<-one_hot(carbonTrainingTable,naCols=FALSE,dropCols=TRUE,dropUnusedLevels=TRUE)
carbonTrainingOneHot
carbonValidationOneHot<-one_hot(carbonValidationTable,naCols=FALSE,dropCols=TRUE,dropUnusedLevels=TRUE)
carbonValidationOneHot
carbonTestingOneHot<-one_hot(carbonTestingTable,naCols=FALSE,dropCols=TRUE,dropUnusedLevels=TRUE)
carbonTestingOneHot
carbonTrainingFinal<-as.data.frame(cbind(carbonTrainingTable[, ..numericCols], carbonTrainingOneHot))
carbonTrainingFinal
carbonValidationFinal<-as.data.frame(cbind(carbonValidationTable[, ..numericCols], carbonValidationOneHot))
carbonValidationFinal
carbonTestingFinal<-as.data.frame(cbind(carbonTestingTable[, ..numericCols], carbonTestingOneHot))
carbonTestingFinallibrary(keras)
model<-keras_model_sequential()%>%
layer_dense(units = 32,activation = "relu",input_shape = dim(carbonTrainingFinal)[2])%>%
layer_dropout(rate=0.3)%>%
layer_dense(units = 32,activation = "relu")%>%
layer_dropout(rate=0.3)%>%
layer_dense(units = 16,activation = "relu")%>%
layer_dropout(rate=0.3)%>%
layer_dense(units = 1)/Users/angadsingh/.virtualenvs/r-tensorflow/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020
warnings.warn(
2024-05-06 09:54:21.056610: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M2 Pro
2024-05-06 09:54:21.056636: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB
2024-05-06 09:54:21.056650: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB
2024-05-06 09:54:21.056883: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:303] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2024-05-06 09:54:21.056910: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:269] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)model %>% compile(
loss="mse",
optimizer=optimizer_adam(lr=0.001)
)WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.history<-model %>% fit(as.matrix(carbonTrainingFinal),
carbonTrainingLabels,
batch_size=50,
epochs=20,
validation_data=list(as.matrix(carbonValidationFinal),carbonValidationLabels)
)Epoch 1/202024-05-06 09:54:21.595932: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
1/145 [..............................] - ETA: 1:06 - loss: 85.0279
11/145 [=>............................] - ETA: 0s - loss: 74.5109
22/145 [===>..........................] - ETA: 0s - loss: 65.8211
33/145 [=====>........................] - ETA: 0s - loss: 58.4659
44/145 [========>.....................] - ETA: 0s - loss: 51.6307
55/145 [==========>...................] - ETA: 0s - loss: 45.7138
66/145 [============>.................] - ETA: 0s - loss: 40.7416
77/145 [==============>...............] - ETA: 0s - loss: 36.7929
88/145 [=================>............] - ETA: 0s - loss: 33.7500
99/145 [===================>..........] - ETA: 0s - loss: 31.1631
110/145 [=====================>........] - ETA: 0s - loss: 29.0696
122/145 [========================>.....] - ETA: 0s - loss: 27.1675
133/145 [==========================>...] - ETA: 0s - loss: 25.6250
144/145 [============================>.] - ETA: 0s - loss: 24.3227
145/145 [==============================] - 1s 5ms/step - loss: 24.31732024-05-06 09:54:22.694010: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
145/145 [==============================] - 2s 10ms/step - loss: 24.3173 - val_loss: 1.5259
Epoch 2/20
1/145 [..............................] - ETA: 1s - loss: 6.6958
11/145 [=>............................] - ETA: 0s - loss: 8.4396
20/145 [===>..........................] - ETA: 0s - loss: 8.1181
31/145 [=====>........................] - ETA: 0s - loss: 8.2299
42/145 [=======>......................] - ETA: 0s - loss: 8.1021
53/145 [=========>....................] - ETA: 0s - loss: 7.8638
65/145 [============>.................] - ETA: 0s - loss: 7.7672
77/145 [==============>...............] - ETA: 0s - loss: 7.6358
88/145 [=================>............] - ETA: 0s - loss: 7.4851
100/145 [===================>..........] - ETA: 0s - loss: 7.3771
111/145 [=====================>........] - ETA: 0s - loss: 7.2641
122/145 [========================>.....] - ETA: 0s - loss: 7.1383
133/145 [==========================>...] - ETA: 0s - loss: 7.0366
145/145 [==============================] - 1s 5ms/step - loss: 6.8839
145/145 [==============================] - 1s 6ms/step - loss: 6.8839 - val_loss: 0.9495
Epoch 3/20
1/145 [..............................] - ETA: 0s - loss: 4.9090
11/145 [=>............................] - ETA: 0s - loss: 5.4185
17/145 [==>...........................] - ETA: 0s - loss: 5.4426
28/145 [====>.........................] - ETA: 0s - loss: 5.2777
39/145 [=======>......................] - ETA: 0s - loss: 5.1862
50/145 [=========>....................] - ETA: 0s - loss: 5.1388
61/145 [===========>..................] - ETA: 0s - loss: 5.0231
72/145 [=============>................] - ETA: 0s - loss: 4.9419
83/145 [================>.............] - ETA: 0s - loss: 4.8610
94/145 [==================>...........] - ETA: 0s - loss: 4.8173
105/145 [====================>.........] - ETA: 0s - loss: 4.7526
116/145 [=======================>......] - ETA: 0s - loss: 4.6912
127/145 [=========================>....] - ETA: 0s - loss: 4.6309
138/145 [===========================>..] - ETA: 0s - loss: 4.5810
145/145 [==============================] - 1s 5ms/step - loss: 4.5351
145/145 [==============================] - 1s 6ms/step - loss: 4.5351 - val_loss: 0.9392
Epoch 4/20
1/145 [..............................] - ETA: 0s - loss: 3.4896
10/145 [=>............................] - ETA: 0s - loss: 3.5051
21/145 [===>..........................] - ETA: 0s - loss: 3.5255
32/145 [=====>........................] - ETA: 0s - loss: 3.3894
43/145 [=======>......................] - ETA: 0s - loss: 3.3336
54/145 [==========>...................] - ETA: 0s - loss: 3.2874
65/145 [============>.................] - ETA: 0s - loss: 3.2213
76/145 [==============>...............] - ETA: 0s - loss: 3.1978
87/145 [=================>............] - ETA: 0s - loss: 3.1708
98/145 [===================>..........] - ETA: 0s - loss: 3.1481
110/145 [=====================>........] - ETA: 0s - loss: 3.1023
121/145 [========================>.....] - ETA: 0s - loss: 3.0936
132/145 [==========================>...] - ETA: 0s - loss: 3.0388
143/145 [============================>.] - ETA: 0s - loss: 3.0222
145/145 [==============================] - 1s 5ms/step - loss: 3.0253
145/145 [==============================] - 1s 6ms/step - loss: 3.0253 - val_loss: 0.7579
Epoch 5/20
1/145 [..............................] - ETA: 1s - loss: 2.7260
10/145 [=>............................] - ETA: 0s - loss: 2.6184
21/145 [===>..........................] - ETA: 0s - loss: 2.4973
32/145 [=====>........................] - ETA: 0s - loss: 2.4977
43/145 [=======>......................] - ETA: 0s - loss: 2.4523
54/145 [==========>...................] - ETA: 0s - loss: 2.4487
65/145 [============>.................] - ETA: 0s - loss: 2.4131
76/145 [==============>...............] - ETA: 0s - loss: 2.3675
87/145 [=================>............] - ETA: 0s - loss: 2.3607
98/145 [===================>..........] - ETA: 0s - loss: 2.3361
109/145 [=====================>........] - ETA: 0s - loss: 2.3278
120/145 [=======================>......] - ETA: 0s - loss: 2.3313
131/145 [==========================>...] - ETA: 0s - loss: 2.3136
142/145 [============================>.] - ETA: 0s - loss: 2.2824
145/145 [==============================] - 1s 5ms/step - loss: 2.2719
145/145 [==============================] - 1s 6ms/step - loss: 2.2719 - val_loss: 0.6862
Epoch 6/20
1/145 [..............................] - ETA: 0s - loss: 2.2501
10/145 [=>............................] - ETA: 0s - loss: 1.9509
22/145 [===>..........................] - ETA: 0s - loss: 1.9806
33/145 [=====>........................] - ETA: 0s - loss: 1.8913
44/145 [========>.....................] - ETA: 0s - loss: 1.8718
56/145 [==========>...................] - ETA: 0s - loss: 1.8391
67/145 [============>.................] - ETA: 0s - loss: 1.8632
79/145 [===============>..............] - ETA: 0s - loss: 1.8733
91/145 [=================>............] - ETA: 0s - loss: 1.8459
103/145 [====================>.........] - ETA: 0s - loss: 1.8374
114/145 [======================>.......] - ETA: 0s - loss: 1.8163
126/145 [=========================>....] - ETA: 0s - loss: 1.7893
137/145 [===========================>..] - ETA: 0s - loss: 1.7672
145/145 [==============================] - 1s 5ms/step - loss: 1.7592
145/145 [==============================] - 1s 6ms/step - loss: 1.7592 - val_loss: 0.4138
Epoch 7/20
1/145 [..............................] - ETA: 1s - loss: 2.0760
11/145 [=>............................] - ETA: 0s - loss: 1.5671
23/145 [===>..........................] - ETA: 0s - loss: 1.4684
34/145 [======>.......................] - ETA: 0s - loss: 1.4511
45/145 [========>.....................] - ETA: 0s - loss: 1.4079
52/145 [=========>....................] - ETA: 0s - loss: 1.3938
64/145 [============>.................] - ETA: 0s - loss: 1.3979
75/145 [==============>...............] - ETA: 0s - loss: 1.3779
86/145 [================>.............] - ETA: 0s - loss: 1.3946
94/145 [==================>...........] - ETA: 0s - loss: 1.3751
105/145 [====================>.........] - ETA: 0s - loss: 1.3624
116/145 [=======================>......] - ETA: 0s - loss: 1.3617
127/145 [=========================>....] - ETA: 0s - loss: 1.3410
138/145 [===========================>..] - ETA: 0s - loss: 1.3376
145/145 [==============================] - 1s 5ms/step - loss: 1.3315
145/145 [==============================] - 1s 6ms/step - loss: 1.3315 - val_loss: 0.2116
Epoch 8/20
1/145 [..............................] - ETA: 1s - loss: 1.4665
10/145 [=>............................] - ETA: 0s - loss: 1.0798
21/145 [===>..........................] - ETA: 0s - loss: 1.0944
32/145 [=====>........................] - ETA: 0s - loss: 1.0913
43/145 [=======>......................] - ETA: 0s - loss: 1.0775
54/145 [==========>...................] - ETA: 0s - loss: 1.0880
65/145 [============>.................] - ETA: 0s - loss: 1.0771
76/145 [==============>...............] - ETA: 0s - loss: 1.0927
87/145 [=================>............] - ETA: 0s - loss: 1.0584
98/145 [===================>..........] - ETA: 0s - loss: 1.0436
108/145 [=====================>........] - ETA: 0s - loss: 1.0315
119/145 [=======================>......] - ETA: 0s - loss: 1.0144
130/145 [=========================>....] - ETA: 0s - loss: 1.0013
141/145 [============================>.] - ETA: 0s - loss: 0.9879
145/145 [==============================] - 1s 5ms/step - loss: 0.9829
145/145 [==============================] - 1s 6ms/step - loss: 0.9829 - val_loss: 0.1225
Epoch 9/20
1/145 [..............................] - ETA: 1s - loss: 1.2694
11/145 [=>............................] - ETA: 0s - loss: 0.8976
22/145 [===>..........................] - ETA: 0s - loss: 0.8978
33/145 [=====>........................] - ETA: 0s - loss: 0.8537
44/145 [========>.....................] - ETA: 0s - loss: 0.8798
55/145 [==========>...................] - ETA: 0s - loss: 0.8644
66/145 [============>.................] - ETA: 0s - loss: 0.8475
77/145 [==============>...............] - ETA: 0s - loss: 0.8401
88/145 [=================>............] - ETA: 0s - loss: 0.8332
99/145 [===================>..........] - ETA: 0s - loss: 0.8231
110/145 [=====================>........] - ETA: 0s - loss: 0.8223
121/145 [========================>.....] - ETA: 0s - loss: 0.8254
132/145 [==========================>...] - ETA: 0s - loss: 0.8216
143/145 [============================>.] - ETA: 0s - loss: 0.8138
145/145 [==============================] - 1s 5ms/step - loss: 0.8132
145/145 [==============================] - 1s 6ms/step - loss: 0.8132 - val_loss: 0.1366
Epoch 10/20
1/145 [..............................] - ETA: 0s - loss: 0.6305
11/145 [=>............................] - ETA: 0s - loss: 0.6996
22/145 [===>..........................] - ETA: 0s - loss: 0.7147
33/145 [=====>........................] - ETA: 0s - loss: 0.7224
44/145 [========>.....................] - ETA: 0s - loss: 0.7073
55/145 [==========>...................] - ETA: 0s - loss: 0.7123
66/145 [============>.................] - ETA: 0s - loss: 0.7080
77/145 [==============>...............] - ETA: 0s - loss: 0.7004
88/145 [=================>............] - ETA: 0s - loss: 0.7014
99/145 [===================>..........] - ETA: 0s - loss: 0.6927
110/145 [=====================>........] - ETA: 0s - loss: 0.6911
121/145 [========================>.....] - ETA: 0s - loss: 0.6912
132/145 [==========================>...] - ETA: 0s - loss: 0.6887
143/145 [============================>.] - ETA: 0s - loss: 0.6831
145/145 [==============================] - 1s 5ms/step - loss: 0.6825
145/145 [==============================] - 1s 6ms/step - loss: 0.6825 - val_loss: 0.0467
Epoch 11/20
1/145 [..............................] - ETA: 0s - loss: 0.6465
11/145 [=>............................] - ETA: 0s - loss: 0.5282
22/145 [===>..........................] - ETA: 0s - loss: 0.5759
34/145 [======>.......................] - ETA: 0s - loss: 0.5810
45/145 [========>.....................] - ETA: 0s - loss: 0.5751
56/145 [==========>...................] - ETA: 0s - loss: 0.5829
68/145 [=============>................] - ETA: 0s - loss: 0.5813
78/145 [===============>..............] - ETA: 0s - loss: 0.5723
89/145 [=================>............] - ETA: 0s - loss: 0.5741
100/145 [===================>..........] - ETA: 0s - loss: 0.5666
111/145 [=====================>........] - ETA: 0s - loss: 0.5653
122/145 [========================>.....] - ETA: 0s - loss: 0.5617
133/145 [==========================>...] - ETA: 0s - loss: 0.5558
140/145 [===========================>..] - ETA: 0s - loss: 0.5559
145/145 [==============================] - 1s 5ms/step - loss: 0.5537
145/145 [==============================] - 1s 6ms/step - loss: 0.5537 - val_loss: 0.0922
Epoch 12/20
1/145 [..............................] - ETA: 1s - loss: 0.7519
11/145 [=>............................] - ETA: 0s - loss: 0.6105
21/145 [===>..........................] - ETA: 0s - loss: 0.5371
32/145 [=====>........................] - ETA: 0s - loss: 0.5432
43/145 [=======>......................] - ETA: 0s - loss: 0.5433
54/145 [==========>...................] - ETA: 0s - loss: 0.5447
65/145 [============>.................] - ETA: 0s - loss: 0.5194
76/145 [==============>...............] - ETA: 0s - loss: 0.5137
86/145 [================>.............] - ETA: 0s - loss: 0.5060
97/145 [===================>..........] - ETA: 0s - loss: 0.5085
109/145 [=====================>........] - ETA: 0s - loss: 0.5060
120/145 [=======================>......] - ETA: 0s - loss: 0.5026
131/145 [==========================>...] - ETA: 0s - loss: 0.4971
142/145 [============================>.] - ETA: 0s - loss: 0.4918
145/145 [==============================] - 1s 5ms/step - loss: 0.4909
145/145 [==============================] - 1s 6ms/step - loss: 0.4909 - val_loss: 0.0450
Epoch 13/20
1/145 [..............................] - ETA: 0s - loss: 0.6261
11/145 [=>............................] - ETA: 0s - loss: 0.4821
22/145 [===>..........................] - ETA: 0s - loss: 0.4862
33/145 [=====>........................] - ETA: 0s - loss: 0.5095
44/145 [========>.....................] - ETA: 0s - loss: 0.4789
54/145 [==========>...................] - ETA: 0s - loss: 0.4762
65/145 [============>.................] - ETA: 0s - loss: 0.4710
76/145 [==============>...............] - ETA: 0s - loss: 0.4722
88/145 [=================>............] - ETA: 0s - loss: 0.4706
99/145 [===================>..........] - ETA: 0s - loss: 0.4628
110/145 [=====================>........] - ETA: 0s - loss: 0.4667
121/145 [========================>.....] - ETA: 0s - loss: 0.4659
132/145 [==========================>...] - ETA: 0s - loss: 0.4608
143/145 [============================>.] - ETA: 0s - loss: 0.4581
145/145 [==============================] - 1s 5ms/step - loss: 0.4568
145/145 [==============================] - 1s 6ms/step - loss: 0.4568 - val_loss: 0.0911
Epoch 14/20
1/145 [..............................] - ETA: 0s - loss: 0.4953
10/145 [=>............................] - ETA: 0s - loss: 0.4858
21/145 [===>..........................] - ETA: 0s - loss: 0.4328
32/145 [=====>........................] - ETA: 0s - loss: 0.4474
43/145 [=======>......................] - ETA: 0s - loss: 0.4513
54/145 [==========>...................] - ETA: 0s - loss: 0.4518
65/145 [============>.................] - ETA: 0s - loss: 0.4367
76/145 [==============>...............] - ETA: 0s - loss: 0.4293
87/145 [=================>............] - ETA: 0s - loss: 0.4260
98/145 [===================>..........] - ETA: 0s - loss: 0.4292
109/145 [=====================>........] - ETA: 0s - loss: 0.4227
120/145 [=======================>......] - ETA: 0s - loss: 0.4242
131/145 [==========================>...] - ETA: 0s - loss: 0.4206
142/145 [============================>.] - ETA: 0s - loss: 0.4148
145/145 [==============================] - 1s 5ms/step - loss: 0.4133
145/145 [==============================] - 1s 6ms/step - loss: 0.4133 - val_loss: 0.0772
Epoch 15/20
1/145 [..............................] - ETA: 1s - loss: 0.3225
11/145 [=>............................] - ETA: 0s - loss: 0.4031
22/145 [===>..........................] - ETA: 0s - loss: 0.4304
34/145 [======>.......................] - ETA: 0s - loss: 0.3984
45/145 [========>.....................] - ETA: 0s - loss: 0.3968
57/145 [==========>...................] - ETA: 0s - loss: 0.3994
69/145 [=============>................] - ETA: 0s - loss: 0.3936
80/145 [===============>..............] - ETA: 0s - loss: 0.3884
91/145 [=================>............] - ETA: 0s - loss: 0.3848
102/145 [====================>.........] - ETA: 0s - loss: 0.3820
113/145 [======================>.......] - ETA: 0s - loss: 0.3771
124/145 [========================>.....] - ETA: 0s - loss: 0.3761
135/145 [==========================>...] - ETA: 0s - loss: 0.3766
145/145 [==============================] - 1s 5ms/step - loss: 0.3792
145/145 [==============================] - 1s 6ms/step - loss: 0.3792 - val_loss: 0.0515
Epoch 16/20
1/145 [..............................] - ETA: 0s - loss: 0.4333
7/145 [>.............................] - ETA: 1s - loss: 0.3213
17/145 [==>...........................] - ETA: 0s - loss: 0.3330
28/145 [====>.........................] - ETA: 0s - loss: 0.3366
39/145 [=======>......................] - ETA: 0s - loss: 0.3336
50/145 [=========>....................] - ETA: 0s - loss: 0.3355
61/145 [===========>..................] - ETA: 0s - loss: 0.3441
72/145 [=============>................] - ETA: 0s - loss: 0.3483
83/145 [================>.............] - ETA: 0s - loss: 0.3544
94/145 [==================>...........] - ETA: 0s - loss: 0.3546
105/145 [====================>.........] - ETA: 0s - loss: 0.3547
116/145 [=======================>......] - ETA: 0s - loss: 0.3565
127/145 [=========================>....] - ETA: 0s - loss: 0.3556
138/145 [===========================>..] - ETA: 0s - loss: 0.3537
145/145 [==============================] - 1s 5ms/step - loss: 0.3538
145/145 [==============================] - 1s 6ms/step - loss: 0.3538 - val_loss: 0.0277
Epoch 17/20
1/145 [..............................] - ETA: 1s - loss: 0.4379
10/145 [=>............................] - ETA: 0s - loss: 0.3668
21/145 [===>..........................] - ETA: 0s - loss: 0.3373
32/145 [=====>........................] - ETA: 0s - loss: 0.3471
43/145 [=======>......................] - ETA: 0s - loss: 0.3520
54/145 [==========>...................] - ETA: 0s - loss: 0.3575
65/145 [============>.................] - ETA: 0s - loss: 0.3522
76/145 [==============>...............] - ETA: 0s - loss: 0.3496
88/145 [=================>............] - ETA: 0s - loss: 0.3481
99/145 [===================>..........] - ETA: 0s - loss: 0.3508
110/145 [=====================>........] - ETA: 0s - loss: 0.3473
121/145 [========================>.....] - ETA: 0s - loss: 0.3453
133/145 [==========================>...] - ETA: 0s - loss: 0.3417
144/145 [============================>.] - ETA: 0s - loss: 0.3377
145/145 [==============================] - 1s 5ms/step - loss: 0.3376
145/145 [==============================] - 1s 6ms/step - loss: 0.3376 - val_loss: 0.0400
Epoch 18/20
1/145 [..............................] - ETA: 0s - loss: 0.2621
11/145 [=>............................] - ETA: 0s - loss: 0.3236
22/145 [===>..........................] - ETA: 0s - loss: 0.3026
33/145 [=====>........................] - ETA: 0s - loss: 0.3158
44/145 [========>.....................] - ETA: 0s - loss: 0.3060
52/145 [=========>....................] - ETA: 0s - loss: 0.3050
63/145 [============>.................] - ETA: 0s - loss: 0.3010
74/145 [==============>...............] - ETA: 0s - loss: 0.3037
85/145 [================>.............] - ETA: 0s - loss: 0.3029
97/145 [===================>..........] - ETA: 0s - loss: 0.3011
108/145 [=====================>........] - ETA: 0s - loss: 0.3014
119/145 [=======================>......] - ETA: 0s - loss: 0.3023
130/145 [=========================>....] - ETA: 0s - loss: 0.2992
141/145 [============================>.] - ETA: 0s - loss: 0.2982
145/145 [==============================] - 1s 5ms/step - loss: 0.2986
145/145 [==============================] - 1s 6ms/step - loss: 0.2986 - val_loss: 0.0562
Epoch 19/20
1/145 [..............................] - ETA: 1s - loss: 0.2322
9/145 [>.............................] - ETA: 0s - loss: 0.2712
20/145 [===>..........................] - ETA: 0s - loss: 0.2604
31/145 [=====>........................] - ETA: 0s - loss: 0.2721
42/145 [=======>......................] - ETA: 0s - loss: 0.2718
53/145 [=========>....................] - ETA: 0s - loss: 0.2729
65/145 [============>.................] - ETA: 0s - loss: 0.2858
77/145 [==============>...............] - ETA: 0s - loss: 0.2884
89/145 [=================>............] - ETA: 0s - loss: 0.2912
100/145 [===================>..........] - ETA: 0s - loss: 0.2871
112/145 [======================>.......] - ETA: 0s - loss: 0.2887
123/145 [========================>.....] - ETA: 0s - loss: 0.2868
134/145 [==========================>...] - ETA: 0s - loss: 0.2868
145/145 [==============================] - 1s 5ms/step - loss: 0.2860
145/145 [==============================] - 1s 6ms/step - loss: 0.2860 - val_loss: 0.0389
Epoch 20/20
1/145 [..............................] - ETA: 0s - loss: 0.1757
11/145 [=>............................] - ETA: 0s - loss: 0.2787
22/145 [===>..........................] - ETA: 0s - loss: 0.2725
33/145 [=====>........................] - ETA: 0s - loss: 0.2725
42/145 [=======>......................] - ETA: 0s - loss: 0.2681
53/145 [=========>....................] - ETA: 0s - loss: 0.2618
59/145 [===========>..................] - ETA: 0s - loss: 0.2600
69/145 [=============>................] - ETA: 0s - loss: 0.2630
80/145 [===============>..............] - ETA: 0s - loss: 0.2599
91/145 [=================>............] - ETA: 0s - loss: 0.2597
102/145 [====================>.........] - ETA: 0s - loss: 0.2621
113/145 [======================>.......] - ETA: 0s - loss: 0.2597
124/145 [========================>.....] - ETA: 0s - loss: 0.2573
135/145 [==========================>...] - ETA: 0s - loss: 0.2576
145/145 [==============================] - 1s 5ms/step - loss: 0.2582
145/145 [==============================] - 1s 6ms/step - loss: 0.2582 - val_loss: 0.0932kerasPrediction<-model %>% predict(as.matrix(carbonTestingFinal))2024-05-06 09:54:39.726589: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
1/63 [..............................] - ETA: 4s
35/63 [===============>..............] - ETA: 0s
63/63 [==============================] - 0s 2ms/step
63/63 [==============================] - 0s 2ms/steprmse=function(x,y){
return((mean(x-y)^2)^0.5)
}
rmse(kerasPrediction,carbonTestLabels)[1] 0.2110004MAE(kerasPrediction,carbonTestLabels)[1] 0.2486037rsquared<-sum((kerasPrediction-carbonTestLabels)^2)/sum((carbonTestLabels-mean(carbonTestLabels))^2)
rsquared[1] 0.473026library(tfruns)
runs<-tuning_run(
"carbonEmission.R",
flags=list(
learning_rate=c(0.1,0.5,0.01,0.001),
nodes=c(8,16,32,64,128),
batch_size=c(16,32,64,128),
dropout=c(0.1,0.2,0.3,0.4,0.5),
activation=c("relu")
),sample=0.05
)400 total combinations of flags
(sampled to 20 combinations)yTraining run 1/20 (flags = list(0.5, 128, 128, 0.3, "relu"))
Using run directory runs/2024-05-06T14-54-50Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 09:54:50.919664: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0047s vs `on_train_batch_end` time: 0.0102s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0047s vs `on_train_batch_end` time: 0.0102s). Check your callbacks.
2024-05-06 09:54:53.231240: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.57/57 - 4s - loss: 11.6138 - val_loss: 0.4636 - 4s/epoch - 62ms/step
Epoch 2/20
57/57 - 0s - loss: 2.8228 - val_loss: 0.4192 - 429ms/epoch - 8ms/step
Epoch 3/20
57/57 - 1s - loss: 1.9487 - val_loss: 0.5122 - 965ms/epoch - 17ms/step
Epoch 4/20
57/57 - 1s - loss: 1.4302 - val_loss: 0.3564 - 707ms/epoch - 12ms/step
Epoch 5/20
57/57 - 0s - loss: 1.0963 - val_loss: 0.3395 - 419ms/epoch - 7ms/step
Epoch 6/20
57/57 - 1s - loss: 0.8655 - val_loss: 0.3819 - 699ms/epoch - 12ms/step
Epoch 7/20
57/57 - 1s - loss: 0.6999 - val_loss: 0.2920 - 1s/epoch - 20ms/step
Epoch 8/20
57/57 - 1s - loss: 0.5871 - val_loss: 0.5491 - 704ms/epoch - 12ms/step
Epoch 9/20
57/57 - 0s - loss: 0.5172 - val_loss: 0.4968 - 421ms/epoch - 7ms/step
Epoch 10/20
57/57 - 1s - loss: 0.4489 - val_loss: 0.2608 - 659ms/epoch - 12ms/step
Epoch 11/20
57/57 - 1s - loss: 0.4264 - val_loss: 0.6395 - 698ms/epoch - 12ms/step
Epoch 12/20
57/57 - 1s - loss: 0.3757 - val_loss: 0.5813 - 501ms/epoch - 9ms/step
Epoch 13/20
57/57 - 1s - loss: 0.3551 - val_loss: 0.7431 - 691ms/epoch - 12ms/step
Epoch 14/20
57/57 - 0s - loss: 0.3338 - val_loss: 0.6908 - 445ms/epoch - 8ms/step
Epoch 15/20
57/57 - 1s - loss: 0.3122 - val_loss: 0.6215 - 660ms/epoch - 12ms/step
Epoch 16/20
57/57 - 1s - loss: 0.3136 - val_loss: 0.7349 - 957ms/epoch - 17ms/step
Epoch 17/20
57/57 - 1s - loss: 0.2970 - val_loss: 0.8001 - 908ms/epoch - 16ms/step
Epoch 18/20
57/57 - 0s - loss: 0.2884 - val_loss: 0.9459 - 428ms/epoch - 8ms/step
Epoch 19/20
57/57 - 1s - loss: 0.3036 - val_loss: 0.8340 - 694ms/epoch - 12ms/step
Epoch 20/20
57/57 - 0s - loss: 0.3165 - val_loss: 0.5513 - 455ms/epoch - 8ms/step
Run completed: runs/2024-05-06T14-54-50Z
Training run 2/20 (flags = list(0.5, 8, 128, 0.5, "relu"))
Using run directory runs/2024-05-06T14-55-07Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 09:55:07.670563: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0047s vs `on_train_batch_end` time: 0.3833s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0047s vs `on_train_batch_end` time: 0.3833s). Check your callbacks.
2024-05-06 09:55:10.590925: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.57/57 - 4s - loss: 58.2908 - val_loss: 44.7017 - 4s/epoch - 67ms/step
Epoch 2/20
57/57 - 0s - loss: 44.4467 - val_loss: 32.0859 - 434ms/epoch - 8ms/step
Epoch 3/20
57/57 - 1s - loss: 34.2047 - val_loss: 25.5460 - 969ms/epoch - 17ms/step
Epoch 4/20
57/57 - 1s - loss: 29.2576 - val_loss: 24.3943 - 697ms/epoch - 12ms/step
Epoch 5/20
57/57 - 0s - loss: 26.4547 - val_loss: 24.6113 - 410ms/epoch - 7ms/step
Epoch 6/20
57/57 - 1s - loss: 24.1613 - val_loss: 24.0585 - 696ms/epoch - 12ms/step
Epoch 7/20
57/57 - 1s - loss: 21.2079 - val_loss: 25.2184 - 844ms/epoch - 15ms/step
Epoch 8/20
57/57 - 0s - loss: 18.4006 - val_loss: 28.0411 - 422ms/epoch - 7ms/step
Epoch 9/20
57/57 - 1s - loss: 15.9729 - val_loss: 30.0195 - 912ms/epoch - 16ms/step
Epoch 10/20
57/57 - 0s - loss: 14.3609 - val_loss: 31.8455 - 438ms/epoch - 8ms/step
Epoch 11/20
57/57 - 0s - loss: 13.1000 - val_loss: 32.6619 - 414ms/epoch - 7ms/step
Epoch 12/20
57/57 - 1s - loss: 12.2610 - val_loss: 33.3808 - 931ms/epoch - 16ms/step
Epoch 13/20
57/57 - 1s - loss: 11.6125 - val_loss: 34.1868 - 688ms/epoch - 12ms/step
Epoch 14/20
57/57 - 0s - loss: 11.0342 - val_loss: 33.9786 - 424ms/epoch - 7ms/step
Epoch 15/20
57/57 - 1s - loss: 10.6773 - val_loss: 34.4642 - 710ms/epoch - 12ms/step
Epoch 16/20
57/57 - 0s - loss: 10.2095 - val_loss: 34.4586 - 422ms/epoch - 7ms/step
Epoch 17/20
57/57 - 1s - loss: 9.8543 - val_loss: 34.6746 - 699ms/epoch - 12ms/step
Epoch 18/20
57/57 - 1s - loss: 9.6432 - val_loss: 34.5086 - 833ms/epoch - 15ms/step
Epoch 19/20
57/57 - 0s - loss: 9.3112 - val_loss: 34.7509 - 433ms/epoch - 8ms/step
Epoch 20/20
57/57 - 1s - loss: 9.0224 - val_loss: 34.3791 - 698ms/epoch - 12ms/step
Run completed: runs/2024-05-06T14-55-07Z
Training run 3/20 (flags = list(0.5, 16, 16, 0.1, "relu"))
Using run directory runs/2024-05-06T14-55-23Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 09:55:23.897602: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0053s vs `on_train_batch_end` time: 0.0096s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0053s vs `on_train_batch_end` time: 0.0096s). Check your callbacks.
2024-05-06 09:55:29.146733: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.451/451 - 6s - loss: 7.2410 - val_loss: 1.0509 - 6s/epoch - 14ms/step
Epoch 2/20
451/451 - 3s - loss: 0.9180 - val_loss: 2.2882 - 3s/epoch - 8ms/step
Epoch 3/20
451/451 - 3s - loss: 0.2663 - val_loss: 3.0453 - 3s/epoch - 7ms/step
Epoch 4/20
451/451 - 3s - loss: 0.1253 - val_loss: 3.1514 - 3s/epoch - 7ms/step
Epoch 5/20
451/451 - 3s - loss: 0.0965 - val_loss: 4.3244 - 3s/epoch - 7ms/step
Epoch 6/20
451/451 - 3s - loss: 0.0802 - val_loss: 3.3610 - 3s/epoch - 7ms/step
Epoch 7/20
451/451 - 3s - loss: 0.0667 - val_loss: 3.5523 - 3s/epoch - 7ms/step
Epoch 8/20
451/451 - 3s - loss: 0.0601 - val_loss: 3.6344 - 3s/epoch - 7ms/step
Epoch 9/20
451/451 - 3s - loss: 0.0521 - val_loss: 3.9413 - 3s/epoch - 7ms/step
Epoch 10/20
451/451 - 3s - loss: 0.0468 - val_loss: 3.2592 - 3s/epoch - 7ms/step
Epoch 11/20
451/451 - 3s - loss: 0.0412 - val_loss: 3.7525 - 3s/epoch - 7ms/step
Epoch 12/20
451/451 - 3s - loss: 0.0387 - val_loss: 4.1755 - 3s/epoch - 7ms/step
Epoch 13/20
451/451 - 3s - loss: 0.0367 - val_loss: 3.7769 - 3s/epoch - 7ms/step
Epoch 14/20
451/451 - 3s - loss: 0.0360 - val_loss: 3.3005 - 3s/epoch - 7ms/step
Epoch 15/20
451/451 - 3s - loss: 0.0368 - val_loss: 3.6954 - 3s/epoch - 7ms/step
Epoch 16/20
451/451 - 3s - loss: 0.0402 - val_loss: 3.3595 - 3s/epoch - 6ms/step
Epoch 17/20
451/451 - 3s - loss: 0.0483 - val_loss: 3.6650 - 3s/epoch - 6ms/step
Epoch 18/20
451/451 - 3s - loss: 0.0462 - val_loss: 3.5981 - 3s/epoch - 6ms/step
Epoch 19/20
451/451 - 3s - loss: 0.0549 - val_loss: 3.5653 - 3s/epoch - 6ms/step
Epoch 20/20
451/451 - 3s - loss: 0.0733 - val_loss: 3.1187 - 3s/epoch - 6ms/step
Run completed: runs/2024-05-06T14-55-23Z
Training run 4/20 (flags = list(0.001, 32, 16, 0.3, "relu"))
Using run directory runs/2024-05-06T14-56-27Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 09:56:30.379454: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0047s vs `on_train_batch_begin` time: 0.0334s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0047s vs `on_train_batch_begin` time: 0.0334s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0047s vs `on_train_batch_end` time: 0.0100s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0047s vs `on_train_batch_end` time: 0.0100s). Check your callbacks.
2024-05-06 09:56:33.239231: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.451/451 - 4s - loss: 8.5746 - val_loss: 1.1113 - 4s/epoch - 8ms/step
Epoch 2/20
451/451 - 3s - loss: 1.4460 - val_loss: 1.0462 - 3s/epoch - 7ms/step
Epoch 3/20
451/451 - 3s - loss: 0.9457 - val_loss: 1.1352 - 3s/epoch - 7ms/step
Epoch 4/20
451/451 - 3s - loss: 0.5666 - val_loss: 1.3286 - 3s/epoch - 6ms/step
Epoch 5/20
451/451 - 3s - loss: 0.3588 - val_loss: 1.2885 - 3s/epoch - 6ms/step
Epoch 6/20
451/451 - 3s - loss: 0.2648 - val_loss: 1.2140 - 3s/epoch - 6ms/step
Epoch 7/20
451/451 - 3s - loss: 0.1982 - val_loss: 1.0452 - 3s/epoch - 6ms/step
Epoch 8/20
451/451 - 3s - loss: 0.1835 - val_loss: 0.7532 - 3s/epoch - 6ms/step
Epoch 9/20
451/451 - 3s - loss: 0.1912 - val_loss: 1.4421 - 3s/epoch - 7ms/step
Epoch 10/20
451/451 - 3s - loss: 0.3299 - val_loss: 1.2238 - 3s/epoch - 6ms/step
Epoch 11/20
451/451 - 3s - loss: 0.5239 - val_loss: 1.2345 - 3s/epoch - 6ms/step
Epoch 12/20
451/451 - 3s - loss: 0.9646 - val_loss: 1.5795 - 3s/epoch - 6ms/step
Epoch 13/20
451/451 - 3s - loss: 1.0706 - val_loss: 1.3688 - 3s/epoch - 6ms/step
Epoch 14/20
451/451 - 3s - loss: 1.5272 - val_loss: 1.5513 - 3s/epoch - 6ms/step
Epoch 15/20
451/451 - 3s - loss: 1.3189 - val_loss: 4.9670 - 3s/epoch - 6ms/step
Epoch 16/20
451/451 - 3s - loss: 0.9311 - val_loss: 6.6251 - 3s/epoch - 6ms/step
Epoch 17/20
451/451 - 3s - loss: 1.0078 - val_loss: 11.9357 - 3s/epoch - 6ms/step
Epoch 18/20
451/451 - 3s - loss: 1.7541 - val_loss: 11.9248 - 3s/epoch - 6ms/step
Epoch 19/20
451/451 - 3s - loss: 1.6260 - val_loss: 12.3699 - 3s/epoch - 6ms/step
Epoch 20/20
451/451 - 3s - loss: 1.9291 - val_loss: 20.7996 - 3s/epoch - 6ms/step
Run completed: runs/2024-05-06T14-56-27Z
Training run 5/20 (flags = list(0.1, 32, 128, 0.3, "relu"))
Using run directory runs/2024-05-06T14-57-29Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 09:57:29.769069: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.1481s vs `on_train_batch_end` time: 0.1940s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.1481s vs `on_train_batch_end` time: 0.1940s). Check your callbacks.
2024-05-06 09:57:32.796985: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.57/57 - 4s - loss: 27.3645 - val_loss: 3.1691 - 4s/epoch - 67ms/step
Epoch 2/20
57/57 - 0s - loss: 9.7090 - val_loss: 2.9526 - 431ms/epoch - 8ms/step
Epoch 3/20
57/57 - 1s - loss: 7.3945 - val_loss: 3.3290 - 862ms/epoch - 15ms/step
Epoch 4/20
57/57 - 1s - loss: 6.3382 - val_loss: 3.0386 - 671ms/epoch - 12ms/step
Epoch 5/20
57/57 - 0s - loss: 5.4127 - val_loss: 2.7711 - 423ms/epoch - 7ms/step
Epoch 6/20
57/57 - 1s - loss: 4.6305 - val_loss: 2.7949 - 657ms/epoch - 12ms/step
Epoch 7/20
57/57 - 0s - loss: 3.7744 - val_loss: 2.5571 - 421ms/epoch - 7ms/step
Epoch 8/20
57/57 - 1s - loss: 3.1616 - val_loss: 2.8495 - 826ms/epoch - 14ms/step
Epoch 9/20
57/57 - 1s - loss: 2.7162 - val_loss: 3.3917 - 764ms/epoch - 13ms/step
Epoch 10/20
57/57 - 0s - loss: 2.4613 - val_loss: 3.4641 - 421ms/epoch - 7ms/step
Epoch 11/20
57/57 - 1s - loss: 2.1948 - val_loss: 4.1984 - 662ms/epoch - 12ms/step
Epoch 12/20
57/57 - 0s - loss: 1.8854 - val_loss: 3.9335 - 417ms/epoch - 7ms/step
Epoch 13/20
57/57 - 1s - loss: 1.6623 - val_loss: 4.9280 - 662ms/epoch - 12ms/step
Epoch 14/20
57/57 - 0s - loss: 1.5136 - val_loss: 5.0246 - 421ms/epoch - 7ms/step
Epoch 15/20
57/57 - 1s - loss: 1.4015 - val_loss: 5.3457 - 621ms/epoch - 11ms/step
Epoch 16/20
57/57 - 0s - loss: 1.2787 - val_loss: 5.9332 - 420ms/epoch - 7ms/step
Epoch 17/20
57/57 - 0s - loss: 1.1320 - val_loss: 5.2903 - 418ms/epoch - 7ms/step
Epoch 18/20
57/57 - 1s - loss: 1.0409 - val_loss: 5.9916 - 972ms/epoch - 17ms/step
Epoch 19/20
57/57 - 0s - loss: 0.9905 - val_loss: 5.5906 - 451ms/epoch - 8ms/step
Epoch 20/20
57/57 - 1s - loss: 0.8973 - val_loss: 5.6969 - 634ms/epoch - 11ms/step
Run completed: runs/2024-05-06T14-57-29Z
Training run 6/20 (flags = list(0.1, 8, 128, 0.5, "relu"))
Using run directory runs/2024-05-06T14-57-44Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 09:57:46.559609: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0043s vs `on_train_batch_end` time: 0.0100s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0043s vs `on_train_batch_end` time: 0.0100s). Check your callbacks.
2024-05-06 09:57:47.701597: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.57/57 - 4s - loss: 54.2360 - val_loss: 39.9696 - 4s/epoch - 63ms/step
Epoch 2/20
57/57 - 0s - loss: 42.4228 - val_loss: 29.1418 - 440ms/epoch - 8ms/step
Epoch 3/20
57/57 - 1s - loss: 35.7827 - val_loss: 24.1368 - 823ms/epoch - 14ms/step
Epoch 4/20
57/57 - 0s - loss: 31.3284 - val_loss: 21.2503 - 439ms/epoch - 8ms/step
Epoch 5/20
57/57 - 1s - loss: 28.3253 - val_loss: 20.8096 - 613ms/epoch - 11ms/step
Epoch 6/20
57/57 - 1s - loss: 25.3994 - val_loss: 20.4552 - 642ms/epoch - 11ms/step
Epoch 7/20
57/57 - 0s - loss: 23.2297 - val_loss: 21.8989 - 422ms/epoch - 7ms/step
Epoch 8/20
57/57 - 1s - loss: 21.2513 - val_loss: 22.9363 - 1s/epoch - 18ms/step
Epoch 9/20
57/57 - 0s - loss: 20.3645 - val_loss: 24.5281 - 415ms/epoch - 7ms/step
Epoch 10/20
57/57 - 0s - loss: 19.6282 - val_loss: 24.2801 - 413ms/epoch - 7ms/step
Epoch 11/20
57/57 - 1s - loss: 18.5731 - val_loss: 24.0698 - 643ms/epoch - 11ms/step
Epoch 12/20
57/57 - 0s - loss: 17.1591 - val_loss: 23.1534 - 417ms/epoch - 7ms/step
Epoch 13/20
57/57 - 1s - loss: 16.1036 - val_loss: 22.8076 - 839ms/epoch - 15ms/step
Epoch 14/20
57/57 - 0s - loss: 15.0624 - val_loss: 22.3049 - 416ms/epoch - 7ms/step
Epoch 15/20
57/57 - 1s - loss: 14.0257 - val_loss: 21.9625 - 617ms/epoch - 11ms/step
Epoch 16/20
57/57 - 0s - loss: 13.3588 - val_loss: 21.1181 - 409ms/epoch - 7ms/step
Epoch 17/20
57/57 - 1s - loss: 12.5951 - val_loss: 20.8273 - 624ms/epoch - 11ms/step
Epoch 18/20
57/57 - 1s - loss: 12.1326 - val_loss: 19.8063 - 835ms/epoch - 15ms/step
Epoch 19/20
57/57 - 0s - loss: 11.6216 - val_loss: 19.3653 - 455ms/epoch - 8ms/step
Epoch 20/20
57/57 - 1s - loss: 11.2639 - val_loss: 19.1199 - 662ms/epoch - 12ms/step
Run completed: runs/2024-05-06T14-57-44Z
Training run 7/20 (flags = list(0.001, 8, 64, 0.2, "relu"))
Using run directory runs/2024-05-06T14-57-59Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 09:58:00.117053: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0051s vs `on_train_batch_end` time: 0.0661s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0051s vs `on_train_batch_end` time: 0.0661s). Check your callbacks.
2024-05-06 09:58:03.043125: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.113/113 - 4s - loss: 27.8824 - val_loss: 10.0069 - 4s/epoch - 33ms/step
Epoch 2/20
113/113 - 1s - loss: 14.4011 - val_loss: 7.1101 - 1s/epoch - 11ms/step
Epoch 3/20
113/113 - 1s - loss: 10.9057 - val_loss: 6.6431 - 933ms/epoch - 8ms/step
Epoch 4/20
113/113 - 1s - loss: 7.8081 - val_loss: 7.3311 - 944ms/epoch - 8ms/step
Epoch 5/20
113/113 - 1s - loss: 4.8643 - val_loss: 8.3484 - 1s/epoch - 10ms/step
Epoch 6/20
113/113 - 1s - loss: 3.5000 - val_loss: 8.6690 - 1s/epoch - 10ms/step
Epoch 7/20
113/113 - 1s - loss: 2.9008 - val_loss: 8.8686 - 706ms/epoch - 6ms/step
Epoch 8/20
113/113 - 1s - loss: 2.5954 - val_loss: 8.8508 - 930ms/epoch - 8ms/step
Epoch 9/20
113/113 - 1s - loss: 2.3520 - val_loss: 8.8799 - 922ms/epoch - 8ms/step
Epoch 10/20
113/113 - 1s - loss: 2.2080 - val_loss: 9.0141 - 927ms/epoch - 8ms/step
Epoch 11/20
113/113 - 1s - loss: 2.0546 - val_loss: 8.7873 - 1s/epoch - 10ms/step
Epoch 12/20
113/113 - 1s - loss: 1.8869 - val_loss: 9.1333 - 1s/epoch - 10ms/step
Epoch 13/20
113/113 - 1s - loss: 1.7640 - val_loss: 9.3086 - 950ms/epoch - 8ms/step
Epoch 14/20
113/113 - 1s - loss: 1.6231 - val_loss: 9.0543 - 935ms/epoch - 8ms/step
Epoch 15/20
113/113 - 1s - loss: 1.5163 - val_loss: 9.3269 - 939ms/epoch - 8ms/step
Epoch 16/20
113/113 - 1s - loss: 1.4586 - val_loss: 9.2640 - 933ms/epoch - 8ms/step
Epoch 17/20
113/113 - 1s - loss: 1.3899 - val_loss: 9.9020 - 936ms/epoch - 8ms/step
Epoch 18/20
113/113 - 1s - loss: 1.2809 - val_loss: 9.8456 - 891ms/epoch - 8ms/step
Epoch 19/20
113/113 - 1s - loss: 1.2347 - val_loss: 9.3953 - 703ms/epoch - 6ms/step
Epoch 20/20
113/113 - 1s - loss: 1.2234 - val_loss: 9.6033 - 888ms/epoch - 8ms/step
Run completed: runs/2024-05-06T14-57-59Z
Training run 8/20 (flags = list(0.5, 32, 32, 0.3, "relu"))
Using run directory runs/2024-05-06T14-58-22Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 09:58:22.755810: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0041s vs `on_train_batch_end` time: 0.0099s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0041s vs `on_train_batch_end` time: 0.0099s). Check your callbacks.
2024-05-06 09:58:26.008537: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.226/226 - 4s - loss: 14.1427 - val_loss: 1.4044 - 4s/epoch - 18ms/step
Epoch 2/20
226/226 - 2s - loss: 3.3903 - val_loss: 0.7778 - 2s/epoch - 9ms/step
Epoch 3/20
226/226 - 2s - loss: 1.4087 - val_loss: 1.1622 - 2s/epoch - 9ms/step
Epoch 4/20
226/226 - 1s - loss: 0.8784 - val_loss: 1.3062 - 1s/epoch - 7ms/step
Epoch 5/20
226/226 - 1s - loss: 0.6745 - val_loss: 1.4484 - 1s/epoch - 7ms/step
Epoch 6/20
226/226 - 2s - loss: 0.5957 - val_loss: 1.7683 - 2s/epoch - 7ms/step
Epoch 7/20
226/226 - 1s - loss: 0.5664 - val_loss: 2.2544 - 1s/epoch - 7ms/step
Epoch 8/20
226/226 - 2s - loss: 0.5056 - val_loss: 3.0286 - 2s/epoch - 8ms/step
Epoch 9/20
226/226 - 2s - loss: 0.4943 - val_loss: 3.6171 - 2s/epoch - 7ms/step
Epoch 10/20
226/226 - 2s - loss: 0.4270 - val_loss: 4.5025 - 2s/epoch - 8ms/step
Epoch 11/20
226/226 - 2s - loss: 0.3557 - val_loss: 6.1073 - 2s/epoch - 8ms/step
Epoch 12/20
226/226 - 2s - loss: 0.2998 - val_loss: 6.1265 - 2s/epoch - 7ms/step
Epoch 13/20
226/226 - 2s - loss: 0.2564 - val_loss: 5.8923 - 2s/epoch - 8ms/step
Epoch 14/20
226/226 - 2s - loss: 0.2483 - val_loss: 7.0588 - 2s/epoch - 8ms/step
Epoch 15/20
226/226 - 1s - loss: 0.2262 - val_loss: 7.1452 - 1s/epoch - 7ms/step
Epoch 16/20
226/226 - 2s - loss: 0.2271 - val_loss: 6.2520 - 2s/epoch - 7ms/step
Epoch 17/20
226/226 - 2s - loss: 0.2006 - val_loss: 6.2938 - 2s/epoch - 7ms/step
Epoch 18/20
226/226 - 2s - loss: 0.1824 - val_loss: 6.5048 - 2s/epoch - 7ms/step
Epoch 19/20
226/226 - 1s - loss: 0.1703 - val_loss: 5.4294 - 1s/epoch - 7ms/step
Epoch 20/20
226/226 - 2s - loss: 0.2027 - val_loss: 4.8250 - 2s/epoch - 8ms/step
Run completed: runs/2024-05-06T14-58-22Z
Training run 9/20 (flags = list(0.5, 64, 64, 0.5, "relu"))
Using run directory runs/2024-05-06T14-58-58Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 09:58:58.587922: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0047s vs `on_train_batch_end` time: 0.0089s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0047s vs `on_train_batch_end` time: 0.0089s). Check your callbacks.
2024-05-06 09:59:01.622113: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.113/113 - 4s - loss: 16.3675 - val_loss: 4.4610 - 4s/epoch - 37ms/step
Epoch 2/20
113/113 - 1s - loss: 4.3639 - val_loss: 2.9391 - 1s/epoch - 9ms/step
Epoch 3/20
113/113 - 1s - loss: 2.3112 - val_loss: 2.8172 - 844ms/epoch - 7ms/step
Epoch 4/20
113/113 - 1s - loss: 1.7284 - val_loss: 3.3120 - 714ms/epoch - 6ms/step
Epoch 5/20
113/113 - 1s - loss: 1.3500 - val_loss: 2.7280 - 1s/epoch - 12ms/step
Epoch 6/20
113/113 - 1s - loss: 1.2119 - val_loss: 3.1502 - 1s/epoch - 10ms/step
Epoch 7/20
113/113 - 1s - loss: 1.0716 - val_loss: 3.1872 - 1s/epoch - 10ms/step
Epoch 8/20
113/113 - 1s - loss: 0.9595 - val_loss: 3.0978 - 931ms/epoch - 8ms/step
Epoch 9/20
113/113 - 1s - loss: 0.8513 - val_loss: 2.8730 - 937ms/epoch - 8ms/step
Epoch 10/20
113/113 - 1s - loss: 0.8251 - val_loss: 2.8990 - 922ms/epoch - 8ms/step
Epoch 11/20
113/113 - 1s - loss: 0.7660 - val_loss: 2.9483 - 1s/epoch - 12ms/step
Epoch 12/20
113/113 - 1s - loss: 0.6602 - val_loss: 2.7560 - 940ms/epoch - 8ms/step
Epoch 13/20
113/113 - 1s - loss: 0.5844 - val_loss: 2.8620 - 1s/epoch - 10ms/step
Epoch 14/20
113/113 - 1s - loss: 0.5224 - val_loss: 3.1427 - 1s/epoch - 10ms/step
Epoch 15/20
113/113 - 1s - loss: 0.5022 - val_loss: 3.1201 - 696ms/epoch - 6ms/step
Epoch 16/20
113/113 - 1s - loss: 0.4721 - val_loss: 3.0309 - 946ms/epoch - 8ms/step
Epoch 17/20
113/113 - 1s - loss: 0.4312 - val_loss: 3.3700 - 944ms/epoch - 8ms/step
Epoch 18/20
113/113 - 1s - loss: 0.3745 - val_loss: 3.1685 - 930ms/epoch - 8ms/step
Epoch 19/20
113/113 - 1s - loss: 0.3299 - val_loss: 3.6113 - 1s/epoch - 10ms/step
Epoch 20/20
113/113 - 1s - loss: 0.3444 - val_loss: 3.0329 - 1s/epoch - 10ms/step
Run completed: runs/2024-05-06T14-58-58Z
Training run 10/20 (flags = list(0.001, 32, 16, 0.1, "relu"))
Using run directory runs/2024-05-06T14-59-22Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 09:59:22.860434: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0048s vs `on_train_batch_end` time: 0.0682s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0048s vs `on_train_batch_end` time: 0.0682s). Check your callbacks.
2024-05-06 09:59:27.537747: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.451/451 - 6s - loss: 5.4132 - val_loss: 2.2404 - 6s/epoch - 13ms/step
Epoch 2/20
451/451 - 3s - loss: 0.2787 - val_loss: 2.8976 - 3s/epoch - 7ms/step
Epoch 3/20
451/451 - 3s - loss: 0.1131 - val_loss: 3.4428 - 3s/epoch - 7ms/step
Epoch 4/20
451/451 - 3s - loss: 0.0763 - val_loss: 3.0443 - 3s/epoch - 6ms/step
Epoch 5/20
451/451 - 3s - loss: 0.0570 - val_loss: 3.2611 - 3s/epoch - 6ms/step
Epoch 6/20
451/451 - 3s - loss: 0.0493 - val_loss: 3.5065 - 3s/epoch - 6ms/step
Epoch 7/20
451/451 - 3s - loss: 0.0466 - val_loss: 3.2422 - 3s/epoch - 6ms/step
Epoch 8/20
451/451 - 3s - loss: 0.0528 - val_loss: 3.9538 - 3s/epoch - 6ms/step
Epoch 9/20
451/451 - 3s - loss: 0.1137 - val_loss: 4.0139 - 3s/epoch - 7ms/step
Epoch 10/20
451/451 - 3s - loss: 0.3051 - val_loss: 7.7234 - 3s/epoch - 6ms/step
Epoch 11/20
451/451 - 3s - loss: 0.6024 - val_loss: 4.9395 - 3s/epoch - 6ms/step
Epoch 12/20
451/451 - 3s - loss: 0.4960 - val_loss: 5.7518 - 3s/epoch - 6ms/step
Epoch 13/20
451/451 - 3s - loss: 0.6740 - val_loss: 6.0071 - 3s/epoch - 6ms/step
Epoch 14/20
451/451 - 3s - loss: 0.8054 - val_loss: 5.9459 - 3s/epoch - 6ms/step
Epoch 15/20
451/451 - 3s - loss: 0.8653 - val_loss: 7.6197 - 3s/epoch - 6ms/step
Epoch 16/20
451/451 - 3s - loss: 0.9881 - val_loss: 7.2715 - 3s/epoch - 7ms/step
Epoch 17/20
451/451 - 3s - loss: 1.7702 - val_loss: 4.9933 - 3s/epoch - 7ms/step
Epoch 18/20
451/451 - 3s - loss: 0.8956 - val_loss: 7.7228 - 3s/epoch - 7ms/step
Epoch 19/20
451/451 - 3s - loss: 1.1377 - val_loss: 6.0371 - 3s/epoch - 7ms/step
Epoch 20/20
451/451 - 3s - loss: 0.9618 - val_loss: 4.6644 - 3s/epoch - 7ms/step
Run completed: runs/2024-05-06T14-59-22Z
Training run 11/20 (flags = list(0.001, 128, 128, 0.1, "relu"))
Using run directory runs/2024-05-06T15-00-23Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 10:00:24.091839: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0053s vs `on_train_batch_begin` time: 0.0261s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0053s vs `on_train_batch_begin` time: 0.0261s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0053s vs `on_train_batch_end` time: 0.2079s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0053s vs `on_train_batch_end` time: 0.2079s). Check your callbacks.
2024-05-06 10:00:26.701102: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.57/57 - 4s - loss: 6.7932 - val_loss: 0.3520 - 4s/epoch - 62ms/step
Epoch 2/20
57/57 - 0s - loss: 1.1441 - val_loss: 0.1199 - 433ms/epoch - 8ms/step
Epoch 3/20
57/57 - 1s - loss: 0.9673 - val_loss: 0.0774 - 659ms/epoch - 12ms/step
Epoch 4/20
57/57 - 0s - loss: 0.8415 - val_loss: 0.0567 - 419ms/epoch - 7ms/step
Epoch 5/20
57/57 - 1s - loss: 0.7376 - val_loss: 0.0749 - 646ms/epoch - 11ms/step
Epoch 6/20
57/57 - 0s - loss: 0.6615 - val_loss: 0.0482 - 421ms/epoch - 7ms/step
Epoch 7/20
57/57 - 1s - loss: 0.5997 - val_loss: 0.0830 - 845ms/epoch - 15ms/step
Epoch 8/20
57/57 - 1s - loss: 0.5506 - val_loss: 0.0730 - 653ms/epoch - 11ms/step
Epoch 9/20
57/57 - 0s - loss: 0.4975 - val_loss: 0.1078 - 429ms/epoch - 8ms/step
Epoch 10/20
57/57 - 1s - loss: 0.4809 - val_loss: 0.0800 - 852ms/epoch - 15ms/step
Epoch 11/20
57/57 - 0s - loss: 0.4315 - val_loss: 0.0916 - 435ms/epoch - 8ms/step
Epoch 12/20
57/57 - 1s - loss: 0.4245 - val_loss: 0.0799 - 611ms/epoch - 11ms/step
Epoch 13/20
57/57 - 1s - loss: 0.4424 - val_loss: 0.0902 - 806ms/epoch - 14ms/step
Epoch 14/20
57/57 - 0s - loss: 0.4757 - val_loss: 0.1759 - 422ms/epoch - 7ms/step
Epoch 15/20
57/57 - 1s - loss: 0.5080 - val_loss: 0.0634 - 844ms/epoch - 15ms/step
Epoch 16/20
57/57 - 0s - loss: 0.6966 - val_loss: 0.1159 - 423ms/epoch - 7ms/step
Epoch 17/20
57/57 - 0s - loss: 0.8899 - val_loss: 0.1272 - 420ms/epoch - 7ms/step
Epoch 18/20
57/57 - 1s - loss: 1.2849 - val_loss: 0.7818 - 836ms/epoch - 15ms/step
Epoch 19/20
57/57 - 0s - loss: 1.5460 - val_loss: 0.1634 - 475ms/epoch - 8ms/step
Epoch 20/20
57/57 - 1s - loss: 1.6178 - val_loss: 0.8310 - 595ms/epoch - 10ms/step
Run completed: runs/2024-05-06T15-00-23Z
Training run 12/20 (flags = list(0.1, 16, 32, 0.5, "relu"))
Using run directory runs/2024-05-06T15-00-38Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 10:00:39.227202: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0050s vs `on_train_batch_end` time: 0.0983s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0050s vs `on_train_batch_end` time: 0.0983s). Check your callbacks.
2024-05-06 10:00:42.673791: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.226/226 - 5s - loss: 42.3642 - val_loss: 17.8899 - 5s/epoch - 21ms/step
Epoch 2/20
226/226 - 1s - loss: 16.8195 - val_loss: 16.6061 - 1s/epoch - 7ms/step
Epoch 3/20
226/226 - 2s - loss: 9.7924 - val_loss: 15.2268 - 2s/epoch - 8ms/step
Epoch 4/20
226/226 - 2s - loss: 6.3296 - val_loss: 15.9971 - 2s/epoch - 8ms/step
Epoch 5/20
226/226 - 2s - loss: 4.5965 - val_loss: 15.7332 - 2s/epoch - 8ms/step
Epoch 6/20
226/226 - 2s - loss: 3.5200 - val_loss: 16.1088 - 2s/epoch - 8ms/step
Epoch 7/20
226/226 - 2s - loss: 3.1163 - val_loss: 15.4500 - 2s/epoch - 7ms/step
Epoch 8/20
226/226 - 2s - loss: 2.9552 - val_loss: 16.8626 - 2s/epoch - 8ms/step
Epoch 9/20
226/226 - 2s - loss: 2.9407 - val_loss: 18.6550 - 2s/epoch - 7ms/step
Epoch 10/20
226/226 - 1s - loss: 2.9194 - val_loss: 20.8167 - 1s/epoch - 7ms/step
Epoch 11/20
226/226 - 2s - loss: 2.6893 - val_loss: 21.3925 - 2s/epoch - 7ms/step
Epoch 12/20
226/226 - 1s - loss: 2.3486 - val_loss: 18.9689 - 1s/epoch - 7ms/step
Epoch 13/20
226/226 - 2s - loss: 2.0517 - val_loss: 19.2253 - 2s/epoch - 7ms/step
Epoch 14/20
226/226 - 2s - loss: 1.6478 - val_loss: 20.3939 - 2s/epoch - 8ms/step
Epoch 15/20
226/226 - 2s - loss: 1.4001 - val_loss: 15.6542 - 2s/epoch - 7ms/step
Epoch 16/20
226/226 - 2s - loss: 1.2497 - val_loss: 16.5755 - 2s/epoch - 7ms/step
Epoch 17/20
226/226 - 2s - loss: 0.8850 - val_loss: 16.2725 - 2s/epoch - 9ms/step
Epoch 18/20
226/226 - 2s - loss: 0.7459 - val_loss: 15.4820 - 2s/epoch - 8ms/step
Epoch 19/20
226/226 - 2s - loss: 0.7532 - val_loss: 15.7535 - 2s/epoch - 8ms/step
Epoch 20/20
226/226 - 1s - loss: 0.7799 - val_loss: 15.0892 - 1s/epoch - 7ms/step
Run completed: runs/2024-05-06T15-00-38Z
Training run 13/20 (flags = list(0.001, 16, 128, 0.1, "relu"))
Using run directory runs/2024-05-06T15-01-16Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 10:01:17.193063: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0051s vs `on_train_batch_end` time: 0.0104s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0051s vs `on_train_batch_end` time: 0.0104s). Check your callbacks.
2024-05-06 10:01:19.315139: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.57/57 - 3s - loss: 32.8509 - val_loss: 6.3026 - 3s/epoch - 51ms/step
Epoch 2/20
57/57 - 1s - loss: 7.4565 - val_loss: 1.1933 - 1s/epoch - 20ms/step
Epoch 3/20
57/57 - 1s - loss: 5.2598 - val_loss: 0.8431 - 832ms/epoch - 15ms/step
Epoch 4/20
57/57 - 0s - loss: 4.5574 - val_loss: 0.8813 - 415ms/epoch - 7ms/step
Epoch 5/20
57/57 - 0s - loss: 4.1343 - val_loss: 0.7815 - 414ms/epoch - 7ms/step
Epoch 6/20
57/57 - 1s - loss: 3.7398 - val_loss: 0.7278 - 778ms/epoch - 14ms/step
Epoch 7/20
57/57 - 0s - loss: 3.3985 - val_loss: 0.7768 - 432ms/epoch - 8ms/step
Epoch 8/20
57/57 - 1s - loss: 3.1007 - val_loss: 0.7981 - 659ms/epoch - 12ms/step
Epoch 9/20
57/57 - 0s - loss: 2.7546 - val_loss: 0.9694 - 417ms/epoch - 7ms/step
Epoch 10/20
57/57 - 1s - loss: 2.5242 - val_loss: 1.1810 - 855ms/epoch - 15ms/step
Epoch 11/20
57/57 - 0s - loss: 2.3096 - val_loss: 1.2593 - 422ms/epoch - 7ms/step
Epoch 12/20
57/57 - 0s - loss: 2.0929 - val_loss: 1.4915 - 423ms/epoch - 7ms/step
Epoch 13/20
57/57 - 0s - loss: 1.7844 - val_loss: 1.6409 - 416ms/epoch - 7ms/step
Epoch 14/20
57/57 - 1s - loss: 1.5349 - val_loss: 1.7480 - 836ms/epoch - 15ms/step
Epoch 15/20
57/57 - 0s - loss: 1.2141 - val_loss: 2.3716 - 424ms/epoch - 7ms/step
Epoch 16/20
57/57 - 0s - loss: 1.0039 - val_loss: 2.7482 - 422ms/epoch - 7ms/step
Epoch 17/20
57/57 - 0s - loss: 0.7942 - val_loss: 2.4333 - 443ms/epoch - 8ms/step
Epoch 18/20
57/57 - 1s - loss: 0.6808 - val_loss: 3.0976 - 623ms/epoch - 11ms/step
Epoch 19/20
57/57 - 1s - loss: 0.5678 - val_loss: 3.3911 - 832ms/epoch - 15ms/step
Epoch 20/20
57/57 - 0s - loss: 0.5200 - val_loss: 3.8461 - 444ms/epoch - 8ms/step
Run completed: runs/2024-05-06T15-01-16Z
Training run 14/20 (flags = list(0.5, 128, 32, 0.5, "relu"))
Using run directory runs/2024-05-06T15-01-31Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 10:01:31.902364: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0044s vs `on_train_batch_end` time: 0.0093s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0044s vs `on_train_batch_end` time: 0.0093s). Check your callbacks.
2024-05-06 10:01:35.249219: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.226/226 - 4s - loss: 6.7435 - val_loss: 2.2936 - 4s/epoch - 18ms/step
Epoch 2/20
226/226 - 2s - loss: 0.8185 - val_loss: 2.3855 - 2s/epoch - 8ms/step
Epoch 3/20
226/226 - 2s - loss: 0.4637 - val_loss: 2.3568 - 2s/epoch - 8ms/step
Epoch 4/20
226/226 - 2s - loss: 0.3194 - val_loss: 2.4118 - 2s/epoch - 9ms/step
Epoch 5/20
226/226 - 2s - loss: 0.2394 - val_loss: 1.9110 - 2s/epoch - 7ms/step
Epoch 6/20
226/226 - 2s - loss: 0.1977 - val_loss: 2.2398 - 2s/epoch - 9ms/step
Epoch 7/20
226/226 - 2s - loss: 0.1703 - val_loss: 1.7173 - 2s/epoch - 9ms/step
Epoch 8/20
226/226 - 2s - loss: 0.1412 - val_loss: 1.7953 - 2s/epoch - 9ms/step
Epoch 9/20
226/226 - 2s - loss: 0.1579 - val_loss: 1.9782 - 2s/epoch - 7ms/step
Epoch 10/20
226/226 - 2s - loss: 0.1898 - val_loss: 2.3954 - 2s/epoch - 7ms/step
Epoch 11/20
226/226 - 2s - loss: 0.3629 - val_loss: 2.2133 - 2s/epoch - 9ms/step
Epoch 12/20
226/226 - 2s - loss: 0.2497 - val_loss: 2.1539 - 2s/epoch - 9ms/step
Epoch 13/20
226/226 - 2s - loss: 0.5479 - val_loss: 1.7509 - 2s/epoch - 9ms/step
Epoch 14/20
226/226 - 2s - loss: 0.7239 - val_loss: 1.6447 - 2s/epoch - 9ms/step
Epoch 15/20
226/226 - 2s - loss: 0.7419 - val_loss: 2.2293 - 2s/epoch - 7ms/step
Epoch 16/20
226/226 - 2s - loss: 0.6668 - val_loss: 5.7126 - 2s/epoch - 7ms/step
Epoch 17/20
226/226 - 2s - loss: 1.0296 - val_loss: 4.6779 - 2s/epoch - 9ms/step
Epoch 18/20
226/226 - 2s - loss: 3.1392 - val_loss: 28.5436 - 2s/epoch - 9ms/step
Epoch 19/20
226/226 - 2s - loss: 1.2982 - val_loss: 35.5697 - 2s/epoch - 9ms/step
Epoch 20/20
226/226 - 2s - loss: 1.7788 - val_loss: 31.6501 - 2s/epoch - 8ms/step
Run completed: runs/2024-05-06T15-01-31Z
Training run 15/20 (flags = list(0.5, 16, 32, 0.2, "relu"))
Using run directory runs/2024-05-06T15-02-11Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 10:02:11.399961: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0048s vs `on_train_batch_end` time: 0.0979s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0048s vs `on_train_batch_end` time: 0.0979s). Check your callbacks.
2024-05-06 10:02:14.933991: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.226/226 - 5s - loss: 15.1082 - val_loss: 2.4513 - 5s/epoch - 21ms/step
Epoch 2/20
226/226 - 2s - loss: 4.0246 - val_loss: 2.3613 - 2s/epoch - 7ms/step
Epoch 3/20
226/226 - 1s - loss: 2.3179 - val_loss: 2.7398 - 1s/epoch - 7ms/step
Epoch 4/20
226/226 - 2s - loss: 1.4267 - val_loss: 3.9060 - 2s/epoch - 9ms/step
Epoch 5/20
226/226 - 2s - loss: 0.9689 - val_loss: 4.5371 - 2s/epoch - 7ms/step
Epoch 6/20
226/226 - 2s - loss: 0.6731 - val_loss: 4.8713 - 2s/epoch - 7ms/step
Epoch 7/20
226/226 - 2s - loss: 0.4922 - val_loss: 5.6833 - 2s/epoch - 8ms/step
Epoch 8/20
226/226 - 2s - loss: 0.4046 - val_loss: 6.0810 - 2s/epoch - 8ms/step
Epoch 9/20
226/226 - 2s - loss: 0.3344 - val_loss: 5.9184 - 2s/epoch - 7ms/step
Epoch 10/20
226/226 - 2s - loss: 0.2714 - val_loss: 6.4398 - 2s/epoch - 8ms/step
Epoch 11/20
226/226 - 1s - loss: 0.2485 - val_loss: 6.7110 - 1s/epoch - 7ms/step
Epoch 12/20
226/226 - 2s - loss: 0.2161 - val_loss: 6.0764 - 2s/epoch - 7ms/step
Epoch 13/20
226/226 - 2s - loss: 0.1849 - val_loss: 7.1760 - 2s/epoch - 8ms/step
Epoch 14/20
226/226 - 1s - loss: 0.1611 - val_loss: 6.3526 - 1s/epoch - 6ms/step
Epoch 15/20
226/226 - 1s - loss: 0.1461 - val_loss: 7.2512 - 1s/epoch - 6ms/step
Epoch 16/20
226/226 - 2s - loss: 0.1321 - val_loss: 7.5646 - 2s/epoch - 7ms/step
Epoch 17/20
226/226 - 2s - loss: 0.1162 - val_loss: 6.9163 - 2s/epoch - 7ms/step
Epoch 18/20
226/226 - 2s - loss: 0.1094 - val_loss: 6.8628 - 2s/epoch - 7ms/step
Epoch 19/20
226/226 - 1s - loss: 0.0999 - val_loss: 7.8049 - 1s/epoch - 6ms/step
Epoch 20/20
226/226 - 2s - loss: 0.0961 - val_loss: 6.8945 - 2s/epoch - 7ms/step
Run completed: runs/2024-05-06T15-02-11Z
Training run 16/20 (flags = list(0.001, 8, 32, 0.3, "relu"))
Using run directory runs/2024-05-06T15-02-47Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 10:02:47.420338: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0060s vs `on_train_batch_end` time: 0.2036s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0060s vs `on_train_batch_end` time: 0.2036s). Check your callbacks.
2024-05-06 10:02:51.137654: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.226/226 - 5s - loss: 29.1013 - val_loss: 11.0376 - 5s/epoch - 21ms/step
Epoch 2/20
226/226 - 2s - loss: 10.5182 - val_loss: 13.0675 - 2s/epoch - 7ms/step
Epoch 3/20
226/226 - 2s - loss: 5.5168 - val_loss: 18.3696 - 2s/epoch - 8ms/step
Epoch 4/20
226/226 - 2s - loss: 4.0513 - val_loss: 18.9510 - 2s/epoch - 9ms/step
Epoch 5/20
226/226 - 2s - loss: 3.0583 - val_loss: 20.2050 - 2s/epoch - 8ms/step
Epoch 6/20
226/226 - 2s - loss: 2.1942 - val_loss: 21.3651 - 2s/epoch - 7ms/step
Epoch 7/20
226/226 - 2s - loss: 1.6739 - val_loss: 22.8143 - 2s/epoch - 8ms/step
Epoch 8/20
226/226 - 2s - loss: 1.4806 - val_loss: 22.5425 - 2s/epoch - 8ms/step
Epoch 9/20
226/226 - 2s - loss: 1.2700 - val_loss: 23.3295 - 2s/epoch - 7ms/step
Epoch 10/20
226/226 - 1s - loss: 1.2092 - val_loss: 24.7564 - 1s/epoch - 6ms/step
Epoch 11/20
226/226 - 2s - loss: 1.7698 - val_loss: 28.4375 - 2s/epoch - 8ms/step
Epoch 12/20
226/226 - 2s - loss: 4.3547 - val_loss: 21.3643 - 2s/epoch - 7ms/step
Epoch 13/20
226/226 - 2s - loss: 6.2425 - val_loss: 16.0654 - 2s/epoch - 9ms/step
Epoch 14/20
226/226 - 2s - loss: 3.6671 - val_loss: 16.9212 - 2s/epoch - 8ms/step
Epoch 15/20
226/226 - 2s - loss: 3.5503 - val_loss: 12.9283 - 2s/epoch - 8ms/step
Epoch 16/20
226/226 - 2s - loss: 3.5513 - val_loss: 12.0035 - 2s/epoch - 8ms/step
Epoch 17/20
226/226 - 2s - loss: 3.1212 - val_loss: 13.2702 - 2s/epoch - 7ms/step
Epoch 18/20
226/226 - 2s - loss: 2.6468 - val_loss: 13.0565 - 2s/epoch - 7ms/step
Epoch 19/20
226/226 - 2s - loss: 2.4637 - val_loss: 12.0501 - 2s/epoch - 7ms/step
Epoch 20/20
226/226 - 2s - loss: 2.2244 - val_loss: 12.2772 - 2s/epoch - 7ms/step
Run completed: runs/2024-05-06T15-02-47Z
Training run 17/20 (flags = list(0.001, 128, 32, 0.5, "relu"))
Using run directory runs/2024-05-06T15-03-24Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 10:03:26.549386: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0054s vs `on_train_batch_end` time: 0.0097s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0054s vs `on_train_batch_end` time: 0.0097s). Check your callbacks.
2024-05-06 10:03:28.721349: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.226/226 - 4s - loss: 7.5747 - val_loss: 3.4870 - 4s/epoch - 17ms/step
Epoch 2/20
226/226 - 2s - loss: 0.8784 - val_loss: 3.1515 - 2s/epoch - 10ms/step
Epoch 3/20
226/226 - 2s - loss: 0.4849 - val_loss: 2.5994 - 2s/epoch - 9ms/step
Epoch 4/20
226/226 - 2s - loss: 0.3130 - val_loss: 2.5727 - 2s/epoch - 8ms/step
Epoch 5/20
226/226 - 2s - loss: 0.2393 - val_loss: 2.9651 - 2s/epoch - 8ms/step
Epoch 6/20
226/226 - 1s - loss: 0.1816 - val_loss: 2.4316 - 1s/epoch - 7ms/step
Epoch 7/20
226/226 - 2s - loss: 0.1522 - val_loss: 2.4291 - 2s/epoch - 9ms/step
Epoch 8/20
226/226 - 2s - loss: 0.1375 - val_loss: 2.3906 - 2s/epoch - 7ms/step
Epoch 9/20
226/226 - 2s - loss: 0.1426 - val_loss: 2.6801 - 2s/epoch - 7ms/step
Epoch 10/20
226/226 - 2s - loss: 0.1421 - val_loss: 2.3931 - 2s/epoch - 8ms/step
Epoch 11/20
226/226 - 2s - loss: 0.1620 - val_loss: 1.9415 - 2s/epoch - 8ms/step
Epoch 12/20
226/226 - 2s - loss: 0.1833 - val_loss: 1.9059 - 2s/epoch - 8ms/step
Epoch 13/20
226/226 - 2s - loss: 0.5284 - val_loss: 4.3917 - 2s/epoch - 8ms/step
Epoch 14/20
226/226 - 2s - loss: 1.4037 - val_loss: 2.5431 - 2s/epoch - 7ms/step
Epoch 15/20
226/226 - 1s - loss: 2.9297 - val_loss: 5.9466 - 1s/epoch - 7ms/step
Epoch 16/20
226/226 - 2s - loss: 1.7710 - val_loss: 26.1538 - 2s/epoch - 8ms/step
Epoch 17/20
226/226 - 1s - loss: 2.7779 - val_loss: 19.3276 - 1s/epoch - 7ms/step
Epoch 18/20
226/226 - 2s - loss: 1.8544 - val_loss: 4.2559 - 2s/epoch - 8ms/step
Epoch 19/20
226/226 - 2s - loss: 9.7696 - val_loss: 23.6507 - 2s/epoch - 8ms/step
Epoch 20/20
226/226 - 1s - loss: 0.5702 - val_loss: 36.0097 - 1s/epoch - 7ms/step
Run completed: runs/2024-05-06T15-03-24Z
Training run 18/20 (flags = list(0.5, 32, 64, 0.3, "relu"))
Using run directory runs/2024-05-06T15-04-02Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 10:04:03.283512: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0045s vs `on_train_batch_end` time: 0.0089s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0045s vs `on_train_batch_end` time: 0.0089s). Check your callbacks.
2024-05-06 10:04:06.284323: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.113/113 - 4s - loss: 17.6082 - val_loss: 2.3672 - 4s/epoch - 34ms/step
Epoch 2/20
113/113 - 1s - loss: 6.9668 - val_loss: 1.7528 - 882ms/epoch - 8ms/step
Epoch 3/20
113/113 - 1s - loss: 4.7502 - val_loss: 1.8603 - 711ms/epoch - 6ms/step
Epoch 4/20
113/113 - 1s - loss: 3.1467 - val_loss: 1.7429 - 961ms/epoch - 9ms/step
Epoch 5/20
113/113 - 1s - loss: 2.2308 - val_loss: 1.9816 - 943ms/epoch - 8ms/step
Epoch 6/20
113/113 - 1s - loss: 1.6439 - val_loss: 2.2695 - 936ms/epoch - 8ms/step
Epoch 7/20
113/113 - 1s - loss: 1.1902 - val_loss: 1.7954 - 1s/epoch - 10ms/step
Epoch 8/20
113/113 - 1s - loss: 0.8874 - val_loss: 2.1697 - 707ms/epoch - 6ms/step
Epoch 9/20
113/113 - 1s - loss: 0.7588 - val_loss: 2.3111 - 934ms/epoch - 8ms/step
Epoch 10/20
113/113 - 1s - loss: 0.6286 - val_loss: 2.2440 - 1s/epoch - 12ms/step
Epoch 11/20
113/113 - 1s - loss: 0.5317 - val_loss: 2.3643 - 934ms/epoch - 8ms/step
Epoch 12/20
113/113 - 1s - loss: 0.4632 - val_loss: 2.2526 - 930ms/epoch - 8ms/step
Epoch 13/20
113/113 - 1s - loss: 0.4096 - val_loss: 2.5801 - 954ms/epoch - 8ms/step
Epoch 14/20
113/113 - 1s - loss: 0.3803 - val_loss: 2.1516 - 931ms/epoch - 8ms/step
Epoch 15/20
113/113 - 1s - loss: 0.3494 - val_loss: 2.3517 - 926ms/epoch - 8ms/step
Epoch 16/20
113/113 - 1s - loss: 0.3167 - val_loss: 2.5672 - 933ms/epoch - 8ms/step
Epoch 17/20
113/113 - 1s - loss: 0.3074 - val_loss: 2.8332 - 839ms/epoch - 7ms/step
Epoch 18/20
113/113 - 1s - loss: 0.2822 - val_loss: 2.8748 - 703ms/epoch - 6ms/step
Epoch 19/20
113/113 - 1s - loss: 0.2659 - val_loss: 2.7788 - 947ms/epoch - 8ms/step
Epoch 20/20
113/113 - 1s - loss: 0.2508 - val_loss: 2.8518 - 939ms/epoch - 8ms/step
Run completed: runs/2024-05-06T15-04-02Z
Training run 19/20 (flags = list(0.5, 32, 128, 0.3, "relu"))
Using run directory runs/2024-05-06T15-04-24Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 10:04:25.384704: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0049s vs `on_train_batch_end` time: 0.0095s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0049s vs `on_train_batch_end` time: 0.0095s). Check your callbacks.
2024-05-06 10:04:27.757719: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.57/57 - 3s - loss: 22.1191 - val_loss: 4.0862 - 3s/epoch - 55ms/step
Epoch 2/20
57/57 - 1s - loss: 9.2245 - val_loss: 2.9485 - 1s/epoch - 19ms/step
Epoch 3/20
57/57 - 1s - loss: 7.3146 - val_loss: 2.3437 - 861ms/epoch - 15ms/step
Epoch 4/20
57/57 - 0s - loss: 6.3196 - val_loss: 2.3771 - 424ms/epoch - 7ms/step
Epoch 5/20
57/57 - 0s - loss: 5.4482 - val_loss: 2.4674 - 431ms/epoch - 8ms/step
Epoch 6/20
57/57 - 0s - loss: 4.7752 - val_loss: 2.8429 - 449ms/epoch - 8ms/step
Epoch 7/20
57/57 - 0s - loss: 4.0931 - val_loss: 2.9437 - 432ms/epoch - 8ms/step
Epoch 8/20
57/57 - 1s - loss: 3.6478 - val_loss: 3.2997 - 1s/epoch - 19ms/step
Epoch 9/20
57/57 - 1s - loss: 3.0678 - val_loss: 3.6161 - 676ms/epoch - 12ms/step
Epoch 10/20
57/57 - 0s - loss: 2.6597 - val_loss: 3.8673 - 426ms/epoch - 7ms/step
Epoch 11/20
57/57 - 1s - loss: 2.2568 - val_loss: 4.8779 - 613ms/epoch - 11ms/step
Epoch 12/20
57/57 - 0s - loss: 1.9041 - val_loss: 4.4440 - 468ms/epoch - 8ms/step
Epoch 13/20
57/57 - 1s - loss: 1.6503 - val_loss: 5.6607 - 634ms/epoch - 11ms/step
Epoch 14/20
57/57 - 0s - loss: 1.5280 - val_loss: 6.0256 - 428ms/epoch - 8ms/step
Epoch 15/20
57/57 - 1s - loss: 1.3766 - val_loss: 5.7559 - 625ms/epoch - 11ms/step
Epoch 16/20
57/57 - 1s - loss: 1.2884 - val_loss: 6.3102 - 578ms/epoch - 10ms/step
Epoch 17/20
57/57 - 0s - loss: 1.2674 - val_loss: 5.9422 - 445ms/epoch - 8ms/step
Epoch 18/20
57/57 - 1s - loss: 1.1961 - val_loss: 5.8227 - 851ms/epoch - 15ms/step
Epoch 19/20
57/57 - 0s - loss: 1.1268 - val_loss: 6.9040 - 429ms/epoch - 8ms/step
Epoch 20/20
57/57 - 1s - loss: 1.0341 - val_loss: 6.4695 - 682ms/epoch - 12ms/step
Run completed: runs/2024-05-06T15-04-24Z
Training run 20/20 (flags = list(0.01, 64, 16, 0.2, "relu"))
Using run directory runs/2024-05-06T15-04-40Z
> FLAGS<- flags(
+ flag_numeric("nodes", 32),
+ flag_numeric("batch_size",32),
+ flag_string("activation","relu"),
+ flag_numeric("learning_ra ..." ... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate= .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 10:04:43.116126: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0055s vs `on_train_batch_end` time: 0.0096s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0055s vs `on_train_batch_end` time: 0.0096s). Check your callbacks.
2024-05-06 10:04:45.725073: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.451/451 - 6s - loss: 4.0625 - val_loss: 1.2168 - 6s/epoch - 13ms/step
Epoch 2/20
451/451 - 3s - loss: 0.3220 - val_loss: 1.2219 - 3s/epoch - 7ms/step
Epoch 3/20
451/451 - 3s - loss: 0.1863 - val_loss: 0.8444 - 3s/epoch - 7ms/step
Epoch 4/20
451/451 - 3s - loss: 0.1310 - val_loss: 1.0633 - 3s/epoch - 6ms/step
Epoch 5/20
451/451 - 3s - loss: 0.0993 - val_loss: 1.0770 - 3s/epoch - 7ms/step
Epoch 6/20
451/451 - 3s - loss: 0.0809 - val_loss: 0.9941 - 3s/epoch - 7ms/step
Epoch 7/20
451/451 - 3s - loss: 0.0737 - val_loss: 1.3372 - 3s/epoch - 7ms/step
Epoch 8/20
451/451 - 3s - loss: 0.0978 - val_loss: 0.9655 - 3s/epoch - 7ms/step
Epoch 9/20
451/451 - 3s - loss: 0.2461 - val_loss: 3.0716 - 3s/epoch - 6ms/step
Epoch 10/20
451/451 - 3s - loss: 0.4367 - val_loss: 6.2774 - 3s/epoch - 6ms/step
Epoch 11/20
451/451 - 3s - loss: 0.8453 - val_loss: 21.2237 - 3s/epoch - 6ms/step
Epoch 12/20
451/451 - 3s - loss: 1.3331 - val_loss: 34.7938 - 3s/epoch - 6ms/step
Epoch 13/20
451/451 - 3s - loss: 0.7451 - val_loss: 54.1062 - 3s/epoch - 6ms/step
Epoch 14/20
451/451 - 3s - loss: 1.0797 - val_loss: 82.9386 - 3s/epoch - 6ms/step
Epoch 15/20
451/451 - 3s - loss: 1.4240 - val_loss: 101.5648 - 3s/epoch - 6ms/step
Epoch 16/20
451/451 - 3s - loss: 1.7040 - val_loss: 212.4915 - 3s/epoch - 6ms/step
Epoch 17/20
451/451 - 3s - loss: 1.5936 - val_loss: 225.9463 - 3s/epoch - 6ms/step
Epoch 18/20
451/451 - 3s - loss: 2.6989 - val_loss: 299.2869 - 3s/epoch - 6ms/step
Epoch 19/20
451/451 - 3s - loss: 2.1928 - val_loss: 403.6202 - 3s/epoch - 6ms/step
Epoch 20/20
451/451 - 3s - loss: 4.2743 - val_loss: 442.2154 - 3s/epoch - 6ms/step
Run completed: runs/2024-05-06T15-04-40ZRuns
runs=runs[order(runs$metric_val_loss),]
runsData frame: 20 x 23 # ... with 10 more rows
# ... with 20 more columns:
# flag_nodes, flag_batch_size, flag_activation, flag_learning_rate, flag_dropout, epochs, epochs_completed, metrics, model, loss_function,
# optimizer, learning_rate, script, start, end, completed, output, source_code, context, typeview_run(runs$run_dir[1])Warning: incomplete final line found on '/var/folders/lw/zymjkl5d1g34b21y_8l475p80000gn/T//Rtmps93sC6/file3d375b21f744/source/carbonEmission.R'Warning: incomplete final line found on '/var/folders/lw/zymjkl5d1g34b21y_8l475p80000gn/T//Rtmps93sC6/file3d375b21f744/source/CarbonEmission.R'dim(carbonTrainingFinal)[1] 8001 71dim(carbonValidationFinal)[1] 799 71carbonTrainingFinal<-rbind(carbonTrainingFinal,carbonValidationFinal)
carbonTrainingLabels<-c(carbonTrainingLabels,carbonValidationLabels)
dim(carbonTrainingFinal)[1] 8800 71BestModel<-keras_model_sequential()%>%
layer_dense(units = 64,activation = "relu",input_shape = dim(carbonTrainingFinal)[2])%>%
layer_dropout(rate=0.1)%>%
layer_dense(units = 64,activation = "relu")%>%
layer_dropout(rate=0.1)%>%
layer_dense(units = 64,activation = "relu")%>%
layer_dropout(rate=0.1)%>%
layer_dense(units = 1)
BestModel %>% compile(
loss="mse",
optimizer=optimizer_adam(lr=0.001)
)WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.history<-BestModel %>% fit(as.matrix(carbonTrainingFinal),
carbonTrainingLabels,
batch_size=128,
epochs=20,
validation_data=list(as.matrix(carbonTestingFinal),carbonTestingLabels)
)Epoch 1/202024-05-06 10:06:49.315203: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
1/69 [..............................] - ETA: 27s - loss: 67.1868
10/69 [===>..........................] - ETA: 0s - loss: 50.4680
21/69 [========>.....................] - ETA: 0s - loss: 34.5585
32/69 [============>.................] - ETA: 0s - loss: 24.4306
43/69 [=================>............] - ETA: 0s - loss: 19.2264
54/69 [======================>.......] - ETA: 0s - loss: 15.8056
64/69 [==========================>...] - ETA: 0s - loss: 13.6703
69/69 [==============================] - 1s 6ms/step - loss: 12.87612024-05-06 10:06:50.013372: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
69/69 [==============================] - 2s 16ms/step - loss: 12.8761 - val_loss: 0.4872
Epoch 2/20
1/69 [..............................] - ETA: 0s - loss: 1.9323
11/69 [===>..........................] - ETA: 0s - loss: 1.9930
21/69 [========>.....................] - ETA: 0s - loss: 1.8814
31/69 [============>.................] - ETA: 0s - loss: 1.8593
41/69 [================>.............] - ETA: 0s - loss: 1.8222
52/69 [=====================>........] - ETA: 0s - loss: 1.7954
63/69 [==========================>...] - ETA: 0s - loss: 1.7758
69/69 [==============================] - 0s 5ms/step - loss: 1.7630
69/69 [==============================] - 1s 7ms/step - loss: 1.7630 - val_loss: 0.3016
Epoch 3/20
1/69 [..............................] - ETA: 0s - loss: 1.5298
11/69 [===>..........................] - ETA: 0s - loss: 1.5985
22/69 [========>.....................] - ETA: 0s - loss: 1.6022
32/69 [============>.................] - ETA: 0s - loss: 1.5711
43/69 [=================>............] - ETA: 0s - loss: 1.5530
55/69 [======================>.......] - ETA: 0s - loss: 1.5071
66/69 [===========================>..] - ETA: 0s - loss: 1.4773
69/69 [==============================] - 0s 5ms/step - loss: 1.4701
69/69 [==============================] - 0s 7ms/step - loss: 1.4701 - val_loss: 0.1979
Epoch 4/20
1/69 [..............................] - ETA: 0s - loss: 1.3780
12/69 [====>.........................] - ETA: 0s - loss: 1.4164
23/69 [=========>....................] - ETA: 0s - loss: 1.3816
34/69 [=============>................] - ETA: 0s - loss: 1.3575
45/69 [==================>...........] - ETA: 0s - loss: 1.3516
56/69 [=======================>......] - ETA: 0s - loss: 1.3298
67/69 [============================>.] - ETA: 0s - loss: 1.3234
69/69 [==============================] - 0s 5ms/step - loss: 1.3209
69/69 [==============================] - 0s 7ms/step - loss: 1.3209 - val_loss: 0.1723
Epoch 5/20
1/69 [..............................] - ETA: 0s - loss: 1.5950
12/69 [====>.........................] - ETA: 0s - loss: 1.2468
24/69 [=========>....................] - ETA: 0s - loss: 1.1921
36/69 [==============>...............] - ETA: 0s - loss: 1.1731
48/69 [===================>..........] - ETA: 0s - loss: 1.1598
60/69 [=========================>....] - ETA: 0s - loss: 1.1512
69/69 [==============================] - 0s 5ms/step - loss: 1.1474
69/69 [==============================] - 0s 7ms/step - loss: 1.1474 - val_loss: 0.1564
Epoch 6/20
1/69 [..............................] - ETA: 0s - loss: 1.0523
11/69 [===>..........................] - ETA: 0s - loss: 1.1100
22/69 [========>.....................] - ETA: 0s - loss: 1.0938
33/69 [=============>................] - ETA: 0s - loss: 1.0778
45/69 [==================>...........] - ETA: 0s - loss: 1.0633
56/69 [=======================>......] - ETA: 0s - loss: 1.0653
67/69 [============================>.] - ETA: 0s - loss: 1.0524
69/69 [==============================] - 0s 5ms/step - loss: 1.0502
69/69 [==============================] - 0s 7ms/step - loss: 1.0502 - val_loss: 0.1713
Epoch 7/20
1/69 [..............................] - ETA: 0s - loss: 0.8185
11/69 [===>..........................] - ETA: 0s - loss: 0.9630
22/69 [========>.....................] - ETA: 0s - loss: 0.9563
33/69 [=============>................] - ETA: 0s - loss: 0.9584
45/69 [==================>...........] - ETA: 0s - loss: 0.9490
55/69 [======================>.......] - ETA: 0s - loss: 0.9391
66/69 [===========================>..] - ETA: 0s - loss: 0.9392
69/69 [==============================] - 0s 5ms/step - loss: 0.9343
69/69 [==============================] - 1s 8ms/step - loss: 0.9343 - val_loss: 0.1433
Epoch 8/20
1/69 [..............................] - ETA: 0s - loss: 1.0760
11/69 [===>..........................] - ETA: 0s - loss: 0.8604
23/69 [=========>....................] - ETA: 0s - loss: 0.8879
35/69 [==============>...............] - ETA: 0s - loss: 0.8838
46/69 [===================>..........] - ETA: 0s - loss: 0.8758
58/69 [========================>.....] - ETA: 0s - loss: 0.8631
69/69 [==============================] - 0s 5ms/step - loss: 0.8582
69/69 [==============================] - 0s 7ms/step - loss: 0.8582 - val_loss: 0.1558
Epoch 9/20
1/69 [..............................] - ETA: 0s - loss: 0.7988
11/69 [===>..........................] - ETA: 0s - loss: 0.8231
22/69 [========>.....................] - ETA: 0s - loss: 0.8113
33/69 [=============>................] - ETA: 0s - loss: 0.8156
44/69 [==================>...........] - ETA: 0s - loss: 0.8021
55/69 [======================>.......] - ETA: 0s - loss: 0.7914
66/69 [===========================>..] - ETA: 0s - loss: 0.7827
69/69 [==============================] - 0s 5ms/step - loss: 0.7804
69/69 [==============================] - 0s 7ms/step - loss: 0.7804 - val_loss: 0.1368
Epoch 10/20
1/69 [..............................] - ETA: 0s - loss: 0.8683
10/69 [===>..........................] - ETA: 0s - loss: 0.7987
20/69 [=======>......................] - ETA: 0s - loss: 0.7896
30/69 [============>.................] - ETA: 0s - loss: 0.7798
39/69 [===============>..............] - ETA: 0s - loss: 0.7703
49/69 [====================>.........] - ETA: 0s - loss: 0.7595
59/69 [========================>.....] - ETA: 0s - loss: 0.7550
69/69 [==============================] - 0s 5ms/step - loss: 0.7436
69/69 [==============================] - 1s 8ms/step - loss: 0.7436 - val_loss: 0.1731
Epoch 11/20
1/69 [..............................] - ETA: 0s - loss: 0.6209
10/69 [===>..........................] - ETA: 0s - loss: 0.7128
20/69 [=======>......................] - ETA: 0s - loss: 0.6910
30/69 [============>.................] - ETA: 0s - loss: 0.6880
40/69 [================>.............] - ETA: 0s - loss: 0.6841
51/69 [=====================>........] - ETA: 0s - loss: 0.6828
62/69 [=========================>....] - ETA: 0s - loss: 0.6806
69/69 [==============================] - 0s 5ms/step - loss: 0.6818
69/69 [==============================] - 1s 7ms/step - loss: 0.6818 - val_loss: 0.1619
Epoch 12/20
1/69 [..............................] - ETA: 0s - loss: 0.6595
11/69 [===>..........................] - ETA: 0s - loss: 0.6448
21/69 [========>.....................] - ETA: 0s - loss: 0.6494
32/69 [============>.................] - ETA: 0s - loss: 0.6393
43/69 [=================>............] - ETA: 0s - loss: 0.6370
54/69 [======================>.......] - ETA: 0s - loss: 0.6358
65/69 [===========================>..] - ETA: 0s - loss: 0.6356
69/69 [==============================] - 0s 5ms/step - loss: 0.6363
69/69 [==============================] - 0s 7ms/step - loss: 0.6363 - val_loss: 0.1585
Epoch 13/20
1/69 [..............................] - ETA: 0s - loss: 0.5724
11/69 [===>..........................] - ETA: 0s - loss: 0.6183
22/69 [========>.....................] - ETA: 0s - loss: 0.6164
33/69 [=============>................] - ETA: 0s - loss: 0.6284
44/69 [==================>...........] - ETA: 0s - loss: 0.6211
55/69 [======================>.......] - ETA: 0s - loss: 0.6146
66/69 [===========================>..] - ETA: 0s - loss: 0.6131
69/69 [==============================] - 0s 5ms/step - loss: 0.6141
69/69 [==============================] - 0s 7ms/step - loss: 0.6141 - val_loss: 0.1638
Epoch 14/20
1/69 [..............................] - ETA: 0s - loss: 0.4695
11/69 [===>..........................] - ETA: 0s - loss: 0.5839
22/69 [========>.....................] - ETA: 0s - loss: 0.5765
33/69 [=============>................] - ETA: 0s - loss: 0.5786
44/69 [==================>...........] - ETA: 0s - loss: 0.5766
55/69 [======================>.......] - ETA: 0s - loss: 0.5688
66/69 [===========================>..] - ETA: 0s - loss: 0.5602
69/69 [==============================] - 0s 5ms/step - loss: 0.5608
69/69 [==============================] - 0s 7ms/step - loss: 0.5608 - val_loss: 0.1593
Epoch 15/20
1/69 [..............................] - ETA: 0s - loss: 0.4251
11/69 [===>..........................] - ETA: 0s - loss: 0.5252
22/69 [========>.....................] - ETA: 0s - loss: 0.5172
33/69 [=============>................] - ETA: 0s - loss: 0.5140
44/69 [==================>...........] - ETA: 0s - loss: 0.5078
55/69 [======================>.......] - ETA: 0s - loss: 0.5041
66/69 [===========================>..] - ETA: 0s - loss: 0.4987
69/69 [==============================] - 0s 5ms/step - loss: 0.4981
69/69 [==============================] - 0s 7ms/step - loss: 0.4981 - val_loss: 0.1620
Epoch 16/20
1/69 [..............................] - ETA: 0s - loss: 0.5678
11/69 [===>..........................] - ETA: 0s - loss: 0.4655
22/69 [========>.....................] - ETA: 0s - loss: 0.4634
33/69 [=============>................] - ETA: 0s - loss: 0.4639
44/69 [==================>...........] - ETA: 0s - loss: 0.4649
56/69 [=======================>......] - ETA: 0s - loss: 0.4679
68/69 [============================>.] - ETA: 0s - loss: 0.4645
69/69 [==============================] - 0s 5ms/step - loss: 0.4638
69/69 [==============================] - 0s 7ms/step - loss: 0.4638 - val_loss: 0.2482
Epoch 17/20
1/69 [..............................] - ETA: 0s - loss: 0.4742
5/69 [=>............................] - ETA: 0s - loss: 0.4301
15/69 [=====>........................] - ETA: 0s - loss: 0.4377
26/69 [==========>...................] - ETA: 0s - loss: 0.4242
37/69 [===============>..............] - ETA: 0s - loss: 0.4263
48/69 [===================>..........] - ETA: 0s - loss: 0.4307
59/69 [========================>.....] - ETA: 0s - loss: 0.4260
69/69 [==============================] - 0s 5ms/step - loss: 0.4257
69/69 [==============================] - 1s 8ms/step - loss: 0.4257 - val_loss: 0.2002
Epoch 18/20
1/69 [..............................] - ETA: 0s - loss: 0.4296
10/69 [===>..........................] - ETA: 0s - loss: 0.4093
20/69 [=======>......................] - ETA: 0s - loss: 0.3958
30/69 [============>.................] - ETA: 0s - loss: 0.3844
40/69 [================>.............] - ETA: 0s - loss: 0.3827
50/69 [====================>.........] - ETA: 0s - loss: 0.3863
60/69 [=========================>....] - ETA: 0s - loss: 0.3824
69/69 [==============================] - 0s 5ms/step - loss: 0.3816
69/69 [==============================] - 1s 8ms/step - loss: 0.3816 - val_loss: 0.2666
Epoch 19/20
1/69 [..............................] - ETA: 0s - loss: 0.3391
11/69 [===>..........................] - ETA: 0s - loss: 0.3784
22/69 [========>.....................] - ETA: 0s - loss: 0.3767
33/69 [=============>................] - ETA: 0s - loss: 0.3664
44/69 [==================>...........] - ETA: 0s - loss: 0.3631
54/69 [======================>.......] - ETA: 0s - loss: 0.3566
64/69 [==========================>...] - ETA: 0s - loss: 0.3521
69/69 [==============================] - 0s 5ms/step - loss: 0.3519
69/69 [==============================] - 1s 8ms/step - loss: 0.3519 - val_loss: 0.2001
Epoch 20/20
1/69 [..............................] - ETA: 0s - loss: 0.3680
10/69 [===>..........................] - ETA: 0s - loss: 0.3497
20/69 [=======>......................] - ETA: 0s - loss: 0.3464
30/69 [============>.................] - ETA: 0s - loss: 0.3501
40/69 [================>.............] - ETA: 0s - loss: 0.3428
50/69 [====================>.........] - ETA: 0s - loss: 0.3385
60/69 [=========================>....] - ETA: 0s - loss: 0.3350
69/69 [==============================] - 0s 5ms/step - loss: 0.3350
69/69 [==============================] - 1s 8ms/step - loss: 0.3350 - val_loss: 0.2561predictBestModel<-model %>% predict(as.matrix(carbonTestingFinal))
1/63 [..............................] - ETA: 0s
36/63 [================>.............] - ETA: 0s
63/63 [==============================] - 0s 1ms/step
63/63 [==============================] - 0s 1ms/steprmse=function(x,y){
return((mean(x-y)^2)^0.5)
}
rmse(predictBestModel,carbonTestingLabels)[1] 0.2110004MAE(predictBestModel,carbonTestingLabels)[1] 0.2486037rsquaredBest<-sum((predictBestModel-carbonTestingLabels)^2)/sum((carbonTestingLabels-mean(carbonTestingLabels))^2)
rsquaredBest[1] 0.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